Version:	0.3
Title:	Estimating Controlled Direct Effects for Explaining Causal Findings
Imports:	stats, Formula, glue, Matching, generics, rlang, broom
Depends:	R (≥ 3.5.0)
Suggests:	knitr, rmarkdown, dplyr, tidyverse, ggplot2, reshape2, scales, testthat, nnet, glmnet, hdm, ranger
Description:	A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216> and the telescope matching estimator described in Blackwell and Strezhnev (2020) <doi:10.1111/rssa.12759>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://mattblackwell.github.io/DirectEffects/
BugReports:	https://github.com/mattblackwell/DirectEffects/issues
VignetteBuilder:	knitr
LazyData:	true
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2024-10-02 00:55:14 UTC; mblackwell
Author:	Matthew Blackwell [aut, cre], Avidit Acharya [aut], Maya Sen [aut], Shiro Kuriwaki [aut], Jacob Brown [aut], Anton Strezhnev [aut]
Maintainer:	Matthew Blackwell <mblackwell@gmail.com>
Repository:	CRAN
Date/Publication:	2024-10-04 15:50:08 UTC

Balance diagnostics for Telescope Match objects

Description

Balance diagnostics for Telescope Match objects

Usage

balance.tmatch(object, vars, data, comparison = NULL)

Arguments

Details

Provides matching balance diagnostics for tmatch objects returned by telescope_match

Value

Returns a data frame with the following columns.

• variable: Name of covariate

• before_0: Pre-matching average of the covariate in the mediator == 0 (if first stage balance) or treatment == 0 (if second stage balance) condition

• before_1: Pre-matching average of the covariate in the mediator == 1 (if first stage balance) or treatment == 1 (if second stage balance) condition

• after_0: Post-matching average of the covariate in the mediator == 0 (if first stage balance) or treatment == 0 (if second stage balance) condition

• after_1: Post-matching average of the covariate in the mediator == 1 (if first stage balance) or treatment == 1 (if second stage balance) condition

• before_sd: standard deviation of the outcome (pre-Matching)

• before_diff: Pre-matching covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance).

• before_std_diff: Pre-matching standardized covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance), Equal to Before_Diff/SD.

• after_diff: Post–matching covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance).

• after_std_diff: Post-matching standardized covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance), Equal to Before_Diff/SD.

Balance diagnostics telescope matching

Description

Provides matching balance diagnostics for telescope matching CDE estimators

Usage

balance_table(object, vars, data, comparison = NULL)

Arguments

Value

Returns a data frame with the following columns.

• variable: Name of covariate

• before_0: Pre-matching average of the covariate in the mediator == 0 (if first stage balance) or treatment == 0 (if second stage balance) condition

• before_1: Pre-matching average of the covariate in the mediator == 1 (if first stage balance) or treatment == 1 (if second stage balance) condition

• after_0: Post-matching average of the covariate in the mediator == 0 (if first stage balance) or treatment == 0 (if second stage balance) condition

• after_1: Post-matching average of the covariate in the mediator == 1 (if first stage balance) or treatment == 1 (if second stage balance) condition

• before_sd: standard deviation of the outcome (pre-Matching)

• before_diff: Pre-matching covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance).

• before_std_diff: Pre-matching standardized covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance), Equal to Before_Diff/SD.

• after_diff: Post–matching covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance).

• after_std_diff: Post-matching standardized covariate difference between mediator arms (if first stage balance) or treatment arms (if second stage balance), Equal to Before_Diff/SD.

Coefficient Estimates across Bootstrapped Samples

Description

Performs a simple bootstrap of a fitted DirectEffects model by re-estimating the model with bootstrap samples.

Usage

boots_g(seqg, boots = 1000)

Arguments

Value

An object of type seqgboots which is a matrix with boots rows and columns for each coefficient in the seqg model. Use summary to provide summary statistics, such as mean and quantiles.

Examples

data(ploughs)
s1 <- sequential_g(women_politics ~ plow +
 agricultural_suitability + tropical_climate + large_animals + rugged |
 years_civil_conflict + years_interstate_conflict  + oil_pc +
 european_descent + communist_dummy + polity2_2000 |
 centered_ln_inc + centered_ln_incsq, ploughs)

out.boots <- boots_g(s1, boots = 100)

summary(out.boots)

Bootstrap Uncertainty Estimates for Telescope Matching

Description

Performs a weighted bootstrap procedure for the output of telescope_match.

Usage

boots_tm(obj, boots = 1000, ci_alpha = 0.05)

Arguments

Value

An data.frame with columns 'ci_low' and 'ci_high' which contain the bootstrapped confidence intervals for the estimated ACDEs in obj$tau.

Examples

data(jobcorps)

## Split male/female
jobcorps_female <- subset(jobcorps, female == 1)

## Telescope matching formula - First stage (X and Z)
tm_form <- exhealth30 ~  schobef + trainyrbef + jobeverbef  |
treat | emplq4 + emplq4full | work2year2q


### Estimate ACDE for women holding employment at 0
tm_out <-  telescope_match(
  tm_form,
  data = jobcorps_female,
  L = 3,
  boot = FALSE,
  verbose = TRUE
)

out.boots <- boots_tm(tm_out)

out.boots

Initialize an AIPW CDE estimator

Description

Initializes the specification of a CDE estimator based on an augmented inverse probability weighting approach.

Usage

cde_aipw(trim = c(0.01, 0.99), aipw_blip = TRUE)

Arguments

Initialize an AIPW DID-CDE estimator

Description

Initializes the specification of a difference-in-differences estimator for the CDE based on an augmented inverse probability weighting.

Usage

cde_did_aipw(
  base_mediator,
  trim = c(0.01, 0.99),
  aipw_blip = TRUE,
  on_treated = FALSE
)

Arguments

Details

This function, unlike other CDE estimators in the package, only returns the estimated effects of the first treatment variable. These effects are conditional on the baseline value of the mediator (base_mediator) when on_treated is TRUE. A marginalized CDE estimand is also estimated. When on_treated is FALSE, these estimates are conditional on the entire "treated" history. Identification requirements are slightly different between these two cases. When on_treated is FALSE, the confounders for the mediator cannot be affected by treatment. See Blackwell et al (2022) for more information.

Initialize an IPW CDE estimator

Description

Initializes the specification of a CDE estimator based on an inverse probability weighting approach.

Usage

cde_ipw(hajek = TRUE, trim = c(0.01, 0.99))

Arguments

Initialize an regression imputation CDE estimator

Description

Initializes the specification of a CDE estimator based on an regression imputation approach

Usage

cde_reg_impute(...)

Arguments

Initialize an telescope matching CDE estimator

Description

Initializes the specification of a CDE estimator based on an telescope matching approach

Usage

cde_telescope_match(...)

Arguments

Estimate sensitivity of ACDE estimates under varying levels of unobserved confounding

Description

Estimate how the Average Controlled Direct Effect varies by various levels of unobserved confounding. For each value of unmeasured confounding, summarized as a correlation between residuals, cdesens computes the ACDE. Standard errors are computed by a simple bootstrap.

Usage

cdesens(
  seqg,
  var,
  rho = seq(-0.9, 0.9, by = 0.05),
  bootstrap = c("none", "standard"),
  boots_n = 1000,
  verbose = FALSE,
  ...
)

Arguments

Examples

data(civilwar)


# main formula: Y ~ A + X | Z | M
form_main <- onset ~ ethfrac + lmtnest + ncontig + Oil | warl +
  gdpenl + lpop + polity2l + relfrac | instab

# estimate CDE
direct <- sequential_g(form_main, data = civilwar)

# sensitivity
out_sens <- cdesens(direct, var = "ethfrac")

# plot sensitivity
plot(out_sens)

Data on civil wars and internal conflict from 1945-1999.

Description

A dataset to replicate the analysis in Fearon and Laitin (2003).

A dataset to replicate the analysis in Fearon and Laitin (2003).

Usage

data(civilwar)

data(civilwar)

Format

A data frame with 6610 observations and 69 variables.

A data frame with 6610 observations and 69 variables.

Details

• ccode. COW country id number

• country. country name

• cname. abbreviated country name

• cmark. 1 for first in each country series

• year. start year of war/conflict

• wars. number wars in progress in country year

• war. 1 if war ongoing in country year

• warl. lagged war, w/ 0 for start of country series

• onset. 1 for civil war onset

• ethonset. 1 if onset = 1 & ethwar ~= 0

• durest. estimated war duration

• aim. 1 = rebels aim at center, 3 = aim at exit or autonomy, 2 = mixed or ambig.

• casename. Id for case, usually name of rebel group(s)

• ended. war ends = 1, 0 = ongoing

• ethwar. 0 = not ethnic, 1 = ambig/mixed, 2 = ethnic

• waryrs. war years for each onset

• pop. population, in 1000s

• lpop. log of pop

• polity2. revised polity score

• gdpen. gdp/pop based on pwt5.6, wdi2001,cow energy data

• gdptype. source/type of gdp/pop estimate

• gdpenl. lagged gdpenl, except for first in country series

• lgdpenl1. log of lagged gdpen

• lpopl1. log population, lagged except for first in country series

• region. country's region, based on MAR project

• western. Dummy for Western Democracies & Japan

• eeurop. Dummy for Eastern Europe

• lamerica. Dummy for Latin America

• ssafrica. Dummy for Sub-Saharan Africa

• asia. Dummy for Asia (not including Japan)

• nafrme. Dummy for North Africa/Middle East

• colbrit. Former British colony

• colfra. former French colony

• mtnest. Estimated percent mountainous terrain

• lmtnest. log of mtnest

• elevdiff. high - low elevation, in meters

• Oil. more than 1/3 export revenues from fuels

• ncontig. noncontiguous state

• ethfrac. ethnic frac. based on Soviet Atlas, plus estimates for missing in 1964

• ef. ethnic fractionalization based on Fearon 2002 APSA paper

• plural. share of largest ethnic group (Fearon 2002 APSA)

• second. share of 2nd largest ethnic group (Fearon 2002 APSA)

• numlang. number languages in Ethnologue > min(1

• relfrac. religious fractionalization

• plurrel. size of largest confession

• minrelpc. size of second largest confession

• muslim. percent muslim

• nwstate. 1 in 1st 2 years of state's existence

• polity2l. lagged polity2, except 1st in country series

• instab. > 2 change in Polity measure in last 3 yrs

• anocl. lagged anocracy (-6 < polity2l < 6)

• deml. lagged democracy (polity2l > 5)

• empethfrac. ethfrac coded for colonial empires

• empwarl. warl coded for data with empires

• emponset. onset coded for data with empires

• empgdpenl. gdpenl coded for empires data

• emplpopl. lpopl coded for empires data

• emplmtnest. lmtnest coded for empires data

• empncontig. ncontig coded for empires

• empolity2l. polity2l adjusted for empires (see fn38 in paper)

• sdwars. number Sambanis/Doyle civ wars in progress

• sdonset. onset of Sambanis/Doyle war

• colwars. number Collier/Hoeffler wars in progress

• colonset. onset of Collier/Hoeffler war

• cowwars. number COW civ wars in progress

• cowonset. onset of COW civ war

• cowwarl. 1 if COW war ongoing in last period

• sdwarl. 1 if S/D war ongoing in last period

• colwarl. 1 if C/H war ongoing in last period

• ccode. COW country id number

• country. country name

• cname. abbreviated country name

• cmark. 1 for first in each country series

• year. start year of war/conflict

• wars. number wars in progress in country year

• war. 1 if war ongoing in country year

• warl. lagged war, w/ 0 for start of country series

• onset. 1 for civil war onset

• ethonset. 1 if onset = 1 & ethwar ~= 0

• durest. estimated war duration

• aim. 1 = rebels aim at center, 3 = aim at exit or autonomy, 2 = mixed or ambig.

• casename. Id for case, usually name of rebel group(s)

• ended. war ends = 1, 0 = ongoing

• ethwar. 0 = not ethnic, 1 = ambig/mixed, 2 = ethnic

• waryrs. war years for each onset

• pop. population, in 1000s

• lpop. log of pop

• polity2. revised polity score

• gdpen. gdp/pop based on pwt5.6, wdi2001,cow energy data

• gdptype. source/type of gdp/pop estimate

• gdpenl. lagged gdpenl, except for first in country series

• lgdpenl1. log of lagged gdpen

• lpopl1. log population, lagged except for first in country series

• region. country's region, based on MAR project

• western. Dummy for Western Democracies & Japan

• eeurop. Dummy for Eastern Europe

• lamerica. Dummy for Latin America

• ssafrica. Dummy for Sub-Saharan Africa

• asia. Dummy for Asia (not including Japan)

• nafrme. Dummy for North Africa/Middle East

• colbrit. Former British colony

• colfra. former French colony

• mtnest. Estimated percent mountainous terrain

• lmtnest. log of mtnest

• elevdiff. high - low elevation, in meters

• Oil. more than 1/3 export revenues from fuels

• ncontig. noncontiguous state

• ethfrac. ethnic frac. based on Soviet Atlas, plus estimates for missing in 1964

• ef. ethnic fractionalization based on Fearon 2002 APSA paper

• plural. share of largest ethnic group (Fearon 2002 APSA)

• second. share of 2nd largest ethnic group (Fearon 2002 APSA)

• numlang. number languages in Ethnologue > min(1

• relfrac. religious fractionalization

• plurrel. size of largest confession

• minrelpc. size of second largest confession

• muslim. percent muslim

• nwstate. 1 in 1st 2 years of state's existence

• polity2l. lagged polity2, except 1st in country series

• instab. > 2 change in Polity measure in last 3 yrs

• anocl. lagged anocracy (-6 < polity2l < 6)

• deml. lagged democracy (polity2l > 5)

• empethfrac. ethfrac coded for colonial empires

• empwarl. warl coded for data with empires

• emponset. onset coded for data with empires

• empgdpenl. gdpenl coded for empires data

• emplpopl. lpopl coded for empires data

• emplmtnest. lmtnest coded for empires data

• empncontig. ncontig coded for empires

• empolity2l. polity2l adjusted for empires (see fn38 in paper)

• sdwars. number Sambanis/Doyle civ wars in progress

• sdonset. onset of Sambanis/Doyle war

• colwars. number Collier/Hoeffler wars in progress

• colonset. onset of Collier/Hoeffler war

• cowwars. number COW civ wars in progress

• cowonset. onset of COW civ war

• cowwarl. 1 if COW war ongoing in last period

• sdwarl. 1 if S/D war ongoing in last period

• colwarl. 1 if C/H war ongoing in last period

Source

doi:10.1017/S0003055403000534

doi:10.1017/S0003055403000534

References

Fearon, James D., and David A. Laitin (2003). Ethnicity, Insurgency, and Civil War. American Political Science Review, 97(1), 75-90. doi:10.1017/S0003055403000534

Fearon, James D., and David A. Laitin (2003). Ethnicity, Insurgency, and Civil War. American Political Science Review, 97(1), 75-90. doi:10.1017/S0003055403000534

Fit a specified CDE estimator

Description

Fit a CDE estimator with the engines specified in the model_spec object.

Usage

estimate(
  object,
  formula,
  data,
  subset,
  crossfit = TRUE,
  n_folds,
  n_splits = 1L
)

Arguments

Data on health and employment outcomes measured as part of the U.S. Job Corps employment training experiment.

Description

A dataset to replicate the analysis in Huber (2014).

A dataset to replicate the analysis in Huber (2014).

Usage

data(jobcorps)

data(jobcorps)

Format

A data frame with 10025 observations and 62 variables.

A data frame with 10025 observations and 62 variables.

Details

• treat. 1 = in program group. 0 = in control group.

• schobef. "in school 1yr before eligibility"

• trainyrbef. "training in year before Job Corps"

• jobeverbef. "ever had a job before Job Corps"

• jobyrbef. "job in year before job corps"

• health012. "good or very good health at assignment"

• health0mis. "general health at assignment missing"

• pe_prb0. "physical/emotional problems at assignment"

• pe_prb0mis. "missing - physical/emotional problems at assignment"

• everalc. "ever abused alcohol before assignment"

• alc12. "alcohol abuse one yr after assignment"

• everilldrugs. "ever took illegal drugs before assignment"

• age_cat. "age at application in years 16-24"

• edumis. "education missing"

• eduhigh. "higher education"

• rwhite. "white"

• everarr. "ever arrested before Job Corps"

• hhsize. "household size at assignment"

• hhsizemis. "household size at assignment missing"

• hhinc12. "low household income at assignment"

• hhinc8. "high household income at assignment"

• fdstamp. "received foodstamps in yr before assignment"

• welf1. "once on welfare while growing up"

• welf2. "twice on welfare while growing up"

• publicass. "public assistance in yr before assignment"

• emplq. "worked some time 9-12 months after assignment"

• emplq4full. "worked all the time in 9-12 months after assignment"

• pemplq4. "proportion of weeks worked 9-12 months after assignment"

• pemplq4mis. "missing - proportion of weeks worked 9-12 months after assignment"

• vocq4. "in vocational training 9-12 months after assignment"

• vocq4mis. "missing - in vocational training 9-12 months after assignment"

• health1212. "very good or good health 1 yr after assignment"

• health123. "fair health 1 yr after assignment"

• pe_prb12. "1=phys/emot probs at 12 mths 0=no prob"

• pe_prb12mis. "missing - physical/emotional problems 1 yr after assignment"

• narry1. "number of arrests in year 1"

• numkidhhf1zero. "no own kids living in household 1 yr after assignment"

• numkidhhf1onetwo. "one or two own kids living in household 1 yr after assignment"

• pubhse12. "1=in public housing 1 yr after assignment, 0=not in"

• h_ins12a. "afdc and other transfers one yr after assignment"

• h_ins12amis. "missing - afdc and other transfers one yr after assignment"

• ... other variables as annotated in the source.

• treat. 1 = in program group. 0 = in control group.

• schobef. "in school 1yr before eligibility"

• trainyrbef. "training in year before Job Corps"

• jobeverbef. "ever had a job before Job Corps"

• jobyrbef. "job in year before job corps"

• health012. "good or very good health at assignment"

• health0mis. "general health at assignment missing"

• pe_prb0. "physical/emotional problems at assignment"

• pe_prb0mis. "missing - physical/emotional problems at assignment"

• everalc. "ever abused alcohol before assignment"

• alc12. "alcohol abuse one yr after assignment"

• everilldrugs. "ever took illegal drugs before assignment"

• age_cat. "age at application in years 16-24"

• edumis. "education missing"

• eduhigh. "higher education"

• rwhite. "white"

• everarr. "ever arrested before Job Corps"

• hhsize. "household size at assignment"

• hhsizemis. "household size at assignment missing"

• hhinc12. "low household income at assignment"

• hhinc8. "high household income at assignment"

• fdstamp. "received foodstamps in yr before assignment"

• welf1. "once on welfare while growing up"

• welf2. "twice on welfare while growing up"

• publicass. "public assistance in yr before assignment"

• emplq. "worked some time 9-12 months after assignment"

• emplq4full. "worked all the time in 9-12 months after assignment"

• pemplq4. "proportion of weeks worked 9-12 months after assignment"

• pemplq4mis. "missing - proportion of weeks worked 9-12 months after assignment"

• vocq4. "in vocational training 9-12 months after assignment"

• vocq4mis. "missing - in vocational training 9-12 months after assignment"

• health1212. "very good or good health 1 yr after assignment"

• health123. "fair health 1 yr after assignment"

• pe_prb12. "1=phys/emot probs at 12 mths 0=no prob"

• pe_prb12mis. "missing - physical/emotional problems 1 yr after assignment"

• narry1. "number of arrests in year 1"

• numkidhhf1zero. "no own kids living in household 1 yr after assignment"

• numkidhhf1onetwo. "one or two own kids living in household 1 yr after assignment"

• pubhse12. "1=in public housing 1 yr after assignment, 0=not in"

• h_ins12a. "afdc and other transfers one yr after assignment"

• h_ins12amis. "missing - afdc and other transfers one yr after assignment"

• ... other variables as annotated in the source.

Source

doi:10.1002/jae.2341

doi:10.1002/jae.2341

References

Huber, M. (2014). Identifying causal mechanisms (primarily) based on inverse probability weighting. Journal of Applied Econometrics, 29(6), 920-943. doi:10.1002/jae.2341

Huber, M. (2014). Identifying causal mechanisms (primarily) based on inverse probability weighting. Journal of Applied Econometrics, 29(6), 920-943. doi:10.1002/jae.2341

Specify the outcome regression model for a CDE treatment

Description

Specifies the functional form and estimation engine for an outcome regression of a treatment previously specified by set_treatment() and the past history of covariates.

Usage

outreg_model(
  object,
  formula,
  engine,
  separate = TRUE,
  include_past = TRUE,
  ...
)

Arguments

Plot output from cdesens

Description

Plot output from cdesens

Usage

## S3 method for class 'cdesens'
plot(
  x,
  level = 0.95,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = "Estimated ACDE",
  bty = "n",
  col = "black",
  lwd = 2,
  ci.col = "grey70",
  ref.lines = TRUE,
  ...
)

Arguments

Histograms of matching weights

Description

Histograms of matching weights

Usage

plotDiag.tmatch(object, stage)

Arguments

Details

Provides histograms of the number of times each unit is used as a match given a tmatch object returned by telescope_match

Value

Outputs a 'plot()' object containing the histogram of match counts

Data on historical plough use and the socioeconomic status of women.

Description

A dataset to replicate the analysis in Alesina, Giuliano, and Nunn (2013).

A dataset to replicate the analysis in Alesina, Giuliano, and Nunn (2013).

Usage

data(ploughs)

data(ploughs)

Format

A data frame with 234 observations and 57 variables.

A data frame with 234 observations and 57 variables.

Details

• isocode. 3-letter code for the country.

• flfp2000. Female labor force participation in 2000

• female_ownership. Percent of firms with female ownership (in latest survey year)

• women_politics. Women in Politics in 2000, WDI

• plow. Animal plow cultivation variable (v39): Using Ethnologue - pop weighted

• agricultural_suitability. overall (millets, sorghum, wheat, barley, rye): share defined as suitable

• tropical_climate. Frac land: tropics and subtropics: using Ethnologue - pop weighted

• large_animals. presence of large animals

• political_hierarchies. Jurisdictional hierarchy beyond local community (v33): Using Ethnologue - pop weighted

• economic_complexity. Settlement patterns (v30)

• ln_income. ln (income)

• ln_income_squared. ln (income) ^2

• centered_ln_inc. de-meaned ln_inc

• centered_ln_incsq. de-meaned ln_inc squared

• country. country name

• communist_dummy. Communism indicator variable

• rugged. Ruggedness (Terrain Ruggedness Index, 100 m.)

• years_interstate_conflict. Years of interstate conflict, 1800-2007 - from COW

• serv_va_gdp2000. Value Added in Service/GDP in 2000

• polity2_2000. Polity 2 measure taken from the Polity IV dataset

• oil_pc. oil production/GDP

• ... other variables as annotated in the source.

• isocode. 3-letter code for the country.

• flfp2000. Female labor force participation in 2000

• female_ownership. Percent of firms with female ownership (in latest survey year)

• women_politics. Women in Politics in 2000, WDI

• plow. Animal plow cultivation variable (v39): Using Ethnologue - pop weighted

• agricultural_suitability. overall (millets, sorghum, wheat, barley, rye): share defined as suitable

• tropical_climate. Frac land: tropics and subtropics: using Ethnologue - pop weighted

• large_animals. presence of large animals

• political_hierarchies. Jurisdictional hierarchy beyond local community (v33): Using Ethnologue - pop weighted

• economic_complexity. Settlement patterns (v30)

• ln_income. ln (income)

• ln_income_squared. ln (income) ^2

• centered_ln_inc. de-meaned ln_inc

• centered_ln_incsq. de-meaned ln_inc squared

• country. country name

• communist_dummy. Communism indicator variable

• rugged. Ruggedness (Terrain Ruggedness Index, 100 m.)

• years_interstate_conflict. Years of interstate conflict, 1800-2007 - from COW

• serv_va_gdp2000. Value Added in Service/GDP in 2000

• polity2_2000. Polity 2 measure taken from the Polity IV dataset

• oil_pc. oil production/GDP

• ... other variables as annotated in the source.

Source

doi:10.1093/qje/qjt005

doi:10.1093/qje/qjt005

References

Alesina, A., Giuliano, P., & Nunn, N. (2013). On the Origins of Gender Roles: Women and the Plough. The Quarterly Journal of Economics, 128(2), 469-530. doi:10.1093/qje/qjt005

Alesina, A., Giuliano, P., & Nunn, N. (2013). On the Origins of Gender Roles: Women and the Plough. The Quarterly Journal of Economics, 128(2), 469-530. doi:10.1093/qje/qjt005

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

glance, tidy

Consistent Variance estimator

Description

Called internally in sequential_g

Usage

seq_g_vcov(first_mod, direct_mod, X1, X2, med.vars)

Arguments

Perform linear sequential g-estimation to estimate the controlled direct effect of a treatment net the effect of a mediator.

Description

Perform linear sequential g-estimation to estimate the controlled direct effect of a treatment net the effect of a mediator.

Usage

sequential_g(
  formula,
  data,
  subset,
  weights,
  na.action,
  offset,
  contrasts = NULL,
  verbose = TRUE,
  ...
)

Arguments

Details

The sequential_g function implements the linear sequential g-estimator developed by Vansteelandt (2009) with the consistent variance estimator developed by Acharya, Blackwell, and Sen (2016).

The formula specifies specifies the full first-stage model including treatment, baseline confounders, intermediate confounders, and the mediators. The user places | bars to separate out these different components of the model. For example, the formula should have the form y ~ tr + x1 + x2 | z1 + z2 | m1 + m2. where tr is the name of the treatment variable, x1 and x2 are baseline covariates, z1 and z2 are intermediate covariates, and m1 and m2 are the names of the mediator variables. This last set of variables specify the 'blip-down' or 'demediation' function that is used to remove the average effect of the mediator (possibly interacted) from the outcome to create the blipped-down outcome. This blipped-down outcome is the passed to a standard linear model with the covariates as specified for the direct effects model.

See the references below for more details.

Value

Returns an object of class A "seqg". Similar to the output of a call to lm. Contains the following components:

• coefficients: a vector of named coefficients for the direct effects model.

• residuals: the residuals, that is the blipped-down outcome minus the fitted values.

• rank: the numeric rank of the fitted linear direct effects model.

• fitted.values: the fitted mean values of the direct effects model.

• weights: (only for weighted fits) the specified weights.

• df.residual: the residual degrees of freedom for the direct effects model.

• aliased: logical vector indicating if any of the terms were dropped or aliased due to perfect collinearity.

• terms: the list of terms object used. One for the baseline covariates and treatment (X) and one for the variables in the blip-down model (M).

• formula: the formula object used, possibly modified to drop a constant in the blip-down model.

• call: the matched call.

• na.action: (where relevant) information returned by model.frame of the special handling of NAs.

• xlevels: the levels of the factor variables.

• contrasts: the contrasts used for the factor variables.

• first_mod: the output from the first-stage regression model.

• model: full model frame, including all variables.

• Ytilde: the blipped-down response vector.

• X: the model matrix for the second stage.

• M: the model matrix for demediation/blip-down function.

In addition, non-null fits will have components assign, effects, and qr from the output of lm.fit or lm.wfit, whichever is used.

References

Vansteelandt, S. (2009). Estimating Direct Effects in Cohort and Case-Control Studies. Epidemiology, 20(6), 851-860.

Acharya, Avidit, Blackwell, Matthew, and Sen, Maya. (2016) "Explaining Causal Effects Without Bias: Detecting and Assessing Direct Effects." American Political Science Review 110:3 pp. 512-529

Examples

data(ploughs)

form_main <- women_politics ~ plow +
  agricultural_suitability + tropical_climate + large_animals +
  political_hierarchies + economic_complexity +
  rugged | years_civil_conflict +
  years_interstate_conflict  + oil_pc +
  european_descent + communist_dummy + polity2_2000 +
  serv_va_gdp2000 | centered_ln_inc + centered_ln_incsq

direct <- sequential_g(form_main, ploughs)

summary(direct)

Specifiy a treatment variable for a controlled direct effect

Description

This function specifies a treatment variable in the sequence of treatment variables that define the controlled direct effect of interest.

Usage

set_treatment(
  object,
  treat,
  formula = NULL,
  treat_type = "categorical",
  eval_vals = NULL
)

Arguments

Value

An updated cde_estimator with this information about the treatment specified.

Author(s)

Matthew Blackwell

Computes standard errors and p-values of DirectEffects estimates

Description

Computes standard errors and p-values of DirectEffects estimates

Usage

## S3 method for class 'seqg'
summary(object, ...)

Arguments

Summary of DirectEffect Bootstrap Estimates

Description

Summary of DirectEffect Bootstrap Estimates

Usage

## S3 method for class 'seqgboots'
summary(object, level = 0.95, ...)

Arguments

Summarize telescope match objects

Description

Summarize telescope match objects

Usage

## S3 method for class 'tmatch'
summary(object, ...)

Arguments

Details

summary method for tmatch objects returned by telescope_match

Returns a summary data frame containing the estimate and standard errors from the 'telescope_match' object.

Value

Returns an object of class summary.tmatch. Contains the following components

• call: matched call.

• m_summary: data.frame summarizes the matching ratios ({ratio}), number of units n_1, n_0, and number of matched units (matched_1, matched_0) for each treatment/mediator (term).

• K: K data frame from the object telescope matching output.

• L: L vector from the object telescope matching output.

• a_names: character vector of the names of the treatment/mediator variables used in matching.

• estimates: matrix of estimated ACDEs with and without bias correction and the estimated standard errors.

Perform telescope matching to estimate the controlled direct effect of a binary treatment net the effect of binary mediators

Description

Perform telescope matching to estimate the controlled direct effect of a binary treatment net the effect of binary mediators

Usage

telescope_match(
  formula,
  data,
  caliper = NULL,
  L = 5,
  verbose = TRUE,
  subset,
  contrasts = NULL,
  separate_bc = TRUE,
  ...
)

Arguments

Details

The telescope_match function implements the two-stage "telescope matching" procedure developed by Blackwell and Strezhnev (2021).

The procedure first estimates a demediated outcome using a combination of matching and a regression bias-correction. The data.frame passed to data should be in the wide format so that each row corresponds to a single unit and treatments and covariates from different time periods appear as different columns. The formula argument specifies both the causal ordering of the variables and the regression specifications for the bias correction. It should be of the form Y ~ X1 | A1 | X2 | A2, where Y is the outcome, X1 is a formula of baseline covariates, A1 is a single variable name indicating the binary treatment in the first period, X2 is a formula of covariates in period 2, and A2 is a single variable name indicating treatment in period 2 (which is also sometimes called the mediator). Note that it is possible to add more covariate/treatment pairs for additional time periods.

Under the default separate_bc == TRUE, the function will match for each treatment/mediator based on the the covariates up to that point within levels of past treatments (so for A2 this matching finds units with similar values of X1 and X2 and the same value of A1). Once this matching is complete, the function moves backward through treatments and imputes potential outcomes using matches and bias-correction regressions, which regress the current imputed potential outcome on the past covariates, within levels of the treatment history up to the current period. The functional form comes from the specification in formula. Controlled direct effects of A1 are estimated for every possible combination of future treatments.

When separate_bc is FALSE, the bias correction regressions are not broken out by the treatments/mediators and those variables are simply included as separate regressors as specified in formula. In this setting, interactions between the treatment/mediator and covariates can be added on a selective basis to the covariate block (X1 or X2 and so on) specifications.

Matching is performed using the Match() routine from the Matching package. By default, matching is L-to-1 nearest neighbor with replacement using Mahalanobis distance.

See the references below for more details.

Value

Returns an object of class tmatch. Contains the following components

• call: the matched call.

• formula: formula used to fit the model.

• m_out: list of matching solutions at each time point. Each member of the list has a 'matches' list giving the units matched to that unit, a 'donors' list with the units to which the unit is matched, and a 'tr' vector which is just the treatment vector being matched.

• K: data.frame of indicating how many times a unit has been used as a match, directly in each period and indirectly across periods.

• L: vector of matching ratios used in each period.

• r_out: nested list of regression imputations used in the bias correction. The first level of the list varies across different controlled direct effects (different sequences of future treatments/mediators). Each of these is a list of time periods and each of these time periods is a list of 'yhat_r_0' and 'yhat_r_1' that give the regression predictions for the potential outcomes at that time point when the treatment at that time point is 0 or 1, respectively, along with 'n_coefs' giving the number of coefficients estimated in those models.

• tau: vector of bias-corrected estimates of average controlled direct effects for different vectors of future treatments/mediators.

• tau_raw: vector of standard matching estimates of average controlled direct effects for different vectors of future treatments/mediators without using bias correction.

• tau_se: vector of estimated standard errors for the average controlled direct effects estimates for different vectors of future treatments/mediators.

• tau_i: matrix of individuals contributions to the ACDE estimates (units on rows, different ACDEs on columns). Used for weighted bootstrap.

• included: logical vector indicating if each row of data was included in estimating tau.

• effects: data frame where each row describes the different ACDEs in tau. The active column describes the which variable's direct effect is being assessed and the rest of the columns describe the fixed values of the future treatments/mediators for that ACDE.

• a_names: character vector with the names of the treatment/mediator variables used in estimation.

• caliper: caliper (if any) used in matching to drop distant observations.

References

Blackwell, Matthew, and Strezhnev, Anton (2020) "Telescope Matching: Reducing Model Dependence in the Estimation of Direct Effects." Journal of the Royal Statistical Society (Series A). doi:10.1111/rssa.12759

Examples

data(jobcorps)

## Split male/female
jobcorps_female <- subset(jobcorps, female == 1)

## Telescope matching formula - First stage (X and Z)
tm_form <- exhealth30 ~  schobef + trainyrbef + jobeverbef  |
treat | emplq4 + emplq4full | work2year2q


### Estimate ACDE for women holding employment at 0
tm_out <-  telescope_match(
  tm_form,
  data = jobcorps_female,
  L = 2,
  boot = FALSE,
  verbose = TRUE
)

Data from a randomized experiment on transgender rights.

Description

A dataset from Broockman and Kalla (2016).

Usage

data(transphobia)

Format

A data frame with 501 observations and 19 variables.

Details

• treated. Indicator of transgender rights script (1) vs recycling script (0).

• nondiscrim_law_t3. Support for transgender nondiscrimination law six weeks after treatment.

• therm_trans_t2. Subjective feelings about transgender people three weeks after treatment (0 = cool, 1 = neutral, 2 = warm).

• therm_obama_t1. Feeling thermometer score (0-100) for feeling warmth or coolness toward Barack Obama 3 days after treatment.

• gender_norm_moral_t1. Index of moral attitudes about gender 3 days after treatment.

• nondiscrim_law_t0. Baseline support for transgender nondiscrimination law.

• therm_trans_t0. Baseline subjective feelings about transgender people (0 = cool, 1 = neutral, 2 = warm).

• therm_obama_t0. Baseline feeling thermometer score (0-100) for feeling warmth or coolness toward Barack Obama.

• gender_norm_moral_t0. Baseline index of moral attitudes about gender.

• ideology_t0. Baseline measure of ideology (conservative is higher).

• religious_t0. Baseline measure of religiousity.

• exposure_trans_t0. Baseline indicator for personal exposure to transgender people (1) or not (0).

• pid_t0. Baseline party identification (-3 = Strong Democrat, 3 = Strong Republican).

• vf_democrat. Indicator for if the unit identifies as a Democrat in the voter file (1) or not (0).

• vf_female. Indicator for if the unit identifies as a woman in the voter file (1) or not (0).

• vf_hispanic. Indicator for if the unit identifies as Hispanic in the voter file (1) or not (0).

• vf_black. Indicator for if the unit identifies as Black in the voter file (1) or not (0).

• vf_age. Age of the citizen in the voter file.

• nondiscrim_law_diff. Difference between nondiscrim_law_t3 and nondiscrim_law_t0

Source

doi:10.1126/science.aad9713

References

Broockman, D. & Kalla, J. (2016). Durably reducing transphobia: A field experiment on door-to-door canvassing. Science, 352(6282), 220-224. doi:10.1126/science.aad9713

Specify the propensity score model for a CDE treatment

Description

Specifies the functional form and estimation engine for a treatment previously specified by set_treatment().

Usage

treat_model(object, formula, engine, separate = TRUE, include_past = TRUE, ...)

Arguments

Author(s)

Matthew Blackwell