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.

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.

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.

Adoption of rural bank credit programs among smallholder farmers in Ghana: an average treatment effect estimation of rates of exposure and adoption and their determinants

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arnold Missiame ◽  
Patrick Irungu ◽  
Rose Adhiambo Nyikal ◽  
Grace Darko Appiah-Kubi
Keyword(s):  
Treatment Effect ◽  
Smallholder Farmers ◽  
Household Head ◽  
Sampling Technique ◽  
Average Treatment Effect ◽  
Bank Credit ◽  
Content Type ◽  
Average Treatment ◽  
Credit Programs ◽  
Effect Estimation

PurposeThe study aims to estimate the rates of exposure to, and adoption of, rural bank credit programs by smallholder farmers in rural Ghana and the factors responsible for those rates.Design/methodology/approachThe study used a random sample of 300 smallholder farmers in the Fanteakwa District of Ghana, obtained through the multistage sampling technique. The study also employed the average treatment effects approach to estimate the average treatment effect of farmers’ exposure to rural bank credit programs, on their adoption of such programs.FindingsThe actual adoption rate is approximately 41%, and the potential, conditional on the whole population being aware of rural bank credit programs, is approximately 61%. Accordingly, there is a gap of about 20% in the adoption of rural bank credit programs, and is due to the incomplete exposure of smallholder farmers to the rural bank credit programs. Age of the household head, access to extension services, membership in farmer-based organizations and active savings accounts with a rural bank are the major contributors to smallholder farmer exposure to and the adoption of rural bank credit programs.Originality/valueThe current study is the first of its kind to be conducted in Ghana on rural bank credit programs. It takes into account the extent to which smallholder farmers are exposed to such credit programs and how it influences their decisions to access or adopt.

Developing Standards for Post-Hoc Weighting in Population-Based Survey Experiments

2017 ◽  
Vol 4 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Annie Franco ◽  
Neil Malhotra ◽  
Gabor Simonovits ◽  
L. J. Zigerell
Keyword(s):  
Treatment Effect ◽  
Methodological Issue ◽  
Sample Selection ◽  
Population Based ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Sample Average ◽  
Population Average ◽  
Survey Experiments ◽  
Post Hoc

AbstractWeighting techniques are employed to generalize results from survey experiments to populations of theoretical and substantive interest. Although weighting is often viewed as a second-order methodological issue, these adjustment methods invoke untestable assumptions about the nature of sample selection and potential heterogeneity in the treatment effect. Therefore, although weighting is a useful technique in estimating population quantities, it can introduce bias and also be used as a researcher degree of freedom. We review survey experiments published in three major journals from 2000–2015 and find that there are no standard operating procedures for weighting survey experiments. We argue that all survey experiments should report the sample average treatment effect (SATE). Researchers seeking to generalize to a broader population can weight to estimate the population average treatment effect (PATE), but should discuss the construction and application of weights in a detailed and transparent manner given the possibility that weighting can introduce bias.

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