Bootstrapping inference of average treatment effect in completely randomized experiments with high-dimensional covariates

2021 ◽  
pp. 1-18
Author(s):  
Hanzhong Liu
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
High Dimensional ◽  
Randomized Experiments ◽  
Average Treatment

scholarly journals High-dimensional regression adjustments in randomized experiments

2016 ◽  
Vol 113 (45) ◽  
pp. 12673-12678 ◽  
Author(s):  
Stefan Wager ◽  
Wenfei Du ◽  
Jonathan Taylor ◽  
Robert J. Tibshirani
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
High Dimensional ◽  
Randomized Experiments ◽  
Finite Sample ◽  
Simple Method ◽  
Average Treatment ◽  
Population Average ◽  
High Dimensional Regression ◽  
Effect Estimation

We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample–unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.

OLS and 2SLS in Randomized and Conditionally Randomized Experiments

2018 ◽  
Vol 238 (3-4) ◽  
pp. 243-293 ◽  
Author(s):  
Jason Ansel ◽  
Han Hong ◽  
and Jessie Li
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Adaptive Randomization ◽  
Working Paper ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Effect Parameter ◽  
Multiple Treatments ◽  
Local Average

Abstract We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

2013 ◽  
Vol 1 (1) ◽  
pp. 135-154 ◽  
Author(s):  
Peter M. Aronow ◽  
Joel A. Middleton
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Covariate Adjustment ◽  
Randomized Experiments ◽  
Treatment Assignment ◽  
Unbiased Estimators ◽  
Average Treatment

AbstractWe derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments

2015 ◽  
Vol 6 (1-2) ◽  
Author(s):  
Joel A. Middleton ◽  
Peter M. Aronow
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Unbiased Estimation ◽  
Random Allocation ◽  
Randomized Experiments ◽  
Average Treatment ◽  
Randomized Field Experiment ◽  
The Difference ◽  
Biased Estimators ◽  
Cluster Randomized

AbstractMany estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated variance estimators. We then analyze a cluster-randomized field experiment on voter mobilization in the US, demonstrating that the proposed estimators have precision that is comparable, if not superior, to that of existing, biased estimators of the average treatment effect.

scholarly journals Regression-adjusted average treatment effect estimates in stratified randomized experiments

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 935-948
Author(s):  
Hanzhong Liu ◽  
Yuehan Yang
Keyword(s):  
Treatment Effect ◽  
Asymptotic Variance ◽  
Average Treatment Effect ◽  
Randomized Experiments ◽  
Asymptotically Normal ◽  
Variance Estimators ◽  
Average Treatment ◽  
Effect Estimation ◽  
Regression Adjustment ◽  
And Control

Summary Linear regression is often used in the analysis of randomized experiments to improve treatment effect estimation by adjusting for imbalances of covariates in the treatment and control groups. This article proposes a randomization-based inference framework for regression adjustment in stratified randomized experiments. We re-establish, under mild conditions, the finite-population central limit theorem for a stratified experiment, and we prove that both the stratified difference-in-means estimator and the regression-adjusted average treatment effect estimator are consistent and asymptotically normal; the asymptotic variance of the latter is no greater and typically less than that of the former. We also provide conservative variance estimators that can be used to construct large-sample confidence intervals for the average treatment effect.

scholarly journals Evaluación de impacto académico y social en egresados universitarios

2019 ◽  
Vol 30 (3) ◽  
pp. 695-712
Author(s):  
Gabriel González ◽  
Luisa Díez-Echavarría ◽  
Elkin Zapa ◽  
Danilo Eusse
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Educación Superior ◽  
Educacion Superior ◽  
Average Treatment

Las instituciones de educación superior deben formar a sus estudiantes según requerimientos del contexto en que se desenvuelven, ya que, sobre la base de su desempeño, es donde se medirá si las políticas de desarrollo socioeconómico son efectivas. Para lograrlo, es necesario identificar el impacto de esa educación en sus egresados, y hacer los ajustes necesarios que generen mejora continua. El objetivo de este artículo es estimar el impacto académico y social de egresados del Instituto Tecnológico Metropolitano – Medellín, a través de un análisis multivariado y la estimación del modelo Average Treatment Effect (ATE). Se encontró que la educación ofrecida a esta población ha generado un impacto académico, asociado a los estudios de actualización, y dos impactos sociales, asociados a la situación laboral y al nivel de ingresos percibidos por los egresados. Se recomienda usar esta metodología en otras instituciones, ya que suele arrojar resultados más informativos y precisos que los estudios tradicionales de caracterización, y se puede medir el efecto de cualquier estrategia.

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