Missing Data Imputation, Classification, Prediction and Average Treatment Effect Estimation Via Random Recursive Partitioning

2006 ◽  
Author(s):  
Stefano Maria Iacus ◽  
Giuseppe Porro
Keyword(s):  
Missing Data ◽  
Treatment Effect ◽  
Recursive Partitioning ◽  
Average Treatment Effect ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Average Treatment ◽  
Treatment Effect Estimation ◽  
Classification Prediction ◽  
Effect Estimation

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.

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.

scholarly journals Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 25
Author(s):  
Kyoo il Kim
Keyword(s):  
Propensity Score ◽  
Average Treatment Effect ◽  
Parametric Estimation ◽  
Efficient Estimation ◽  
Nonparametric Method ◽  
Average Treatment ◽  
Treatment Effect Estimation ◽  
Average Treatment Effects ◽  
Effect Estimation ◽  
Parametric Class

It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known. When the true propensity score is unknown but parametric, it is conjectured from the literature that we still need nonparametric propensity score estimation to achieve the efficiency. We formalize this argument and further identify the source of the efficiency loss arising from parametric estimation of the propensity score. We also provide an intuition of why this overfitting is necessary. Our finding suggests that, even when we know that the true propensity score belongs to a parametric class, we still need to estimate the propensity score by a nonparametric method in applications.

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 Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial

2021 ◽  
pp. 0272989X2098654
Author(s):  
Luke Keele ◽  
Stephen O’Neill ◽  
Richard Grieve
Keyword(s):  
Patient Preference ◽  
Treatment Effect ◽  
Medical Management ◽  
Observational Studies ◽  
Treatment Effectiveness ◽  
Average Treatment Effect ◽  
Estimation Methods ◽  
Effect Estimate ◽  
Treatment Effect Estimation ◽  
Effect Estimation

Much evidence in comparative effectiveness research is based on observational studies. Researchers who conduct observational studies typically assume that there are no unobservable differences between the treatment groups under comparison. Treatment effectiveness is estimated after adjusting for observed differences between comparison groups. However, estimates of treatment effectiveness may be biased because of misspecification of the statistical model. That is, if the method of treatment effect estimation imposes unduly strong functional form assumptions, treatment effect estimates may be inaccurate, leading to inappropriate recommendations about treatment decisions. We compare the performance of a wide variety of treatment effect estimation methods for the average treatment effect. We do so within the context of the REFLUX study from the United Kingdom. In REFLUX, participants were enrolled in either an randomized controlled trial (RCT) or an observational study arm. In the RCT, patients were randomly assigned to either surgery or medical management. In the patient preference arm, participants selected to either have surgery or medical management. We attempt to recover the treatment effect estimate from the RCT using the data from the patient preference arms of the study. We vary the method of treatment effect estimation and record which methods are successful and which are not. We apply more than 20 different methods, including standard regression models as well as advanced machine learning methods. We find that simple propensity score matching methods provide the least accurate estimates versus the RCT benchmark. We find variation in performance across the other methods, with some, but not all recovering the experimental benchmark. We conclude that future studies should use multiple methods of estimation to fully represent uncertainty according to the choice of estimation approach.

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