The Minimax Estimator of the Average Treatment Effect, among Linear Combinations of Conditional Average Treatment Effects Estimators

2021 ◽  
Author(s):  
Clément de Chaisemartin
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Linear Combinations ◽  
Minimax Estimator ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Generalizability of heterogeneous treatment effect estimates across samples

2018 ◽  
Vol 115 (49) ◽  
pp. 12441-12446 ◽  
Author(s):  
Alexander Coppock ◽  
Thomas J. Leeper ◽  
Kevin J. Mullinix
Keyword(s):  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Partial Explanation ◽  
Convenience Sample ◽  
Conditional Average ◽  
Replication Studies ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Effect Heterogeneity

The extent to which survey experiments conducted with nonrepresentative convenience samples are generalizable to target populations depends critically on the degree of treatment effect heterogeneity. Recent inquiries have found a strong correspondence between sample average treatment effects estimated in nationally representative experiments and in replication studies conducted with convenience samples. We consider here two possible explanations: low levels of effect heterogeneity or high levels of effect heterogeneity that are unrelated to selection into the convenience sample. We analyze subgroup conditional average treatment effects using 27 original–replication study pairs (encompassing 101,745 individual survey responses) to assess the extent to which subgroup effect estimates generalize. While there are exceptions, the overwhelming pattern that emerges is one of treatment effect homogeneity, providing a partial explanation for strong correspondence across both unconditional and conditional average treatment effect estimates.

The Effect of Extension Services and Credit on Agricultural Production in Bolivia, Peru, and Colombia

2021 ◽  
Author(s):  
Mateus C. R. Neves ◽  
Felipe De Figueiredo Silva ◽  
Carlos Otávio Freitas
Keyword(s):  
Travel Time ◽  
Treatment Effect ◽  
Agricultural Production ◽  
Treatment Effects ◽  
Geographic Information ◽  
Average Treatment Effect ◽  
Extension Services ◽  
Average Treatment ◽  
Andean Countries ◽  
Average Treatment Effects

In this paper we estimate the average treatment effect from access to extension services and credit on agricultural production in selected Andean countries (Bolivia, Peru, and Colombia). More specifically, we want to identify the effect of accessibility, here represented as travel time to the nearest area with 1,500 or more inhabitants per square kilometer or at least 50,000 inhabitants, on the likelihood of accessing extension and credit. To estimate the treatment effect and identify the effect of accessibility on these variables, we use data from the Colombian and Bolivian Agricultural Censuses of 2013 and 2014, respectively; a national agricultural survey from 2017 for Peru; and geographic information on travel time. We find that the average treatment effect for extension is higher compared to that of credit for farms in Bolivia and Peru, and lower for Colombia. The average treatment effects of extension and credit for Peruvian farms are $2,387.45 and $3,583.42 respectively. The average treatment effect for extension and credit are $941.92 and $668.69, respectively, while in Colombia are $1,365.98 and $1,192.51, respectively. We also find that accessibility and the likelihood of accessing these services are nonlinearly related. Results indicate that higher likelihood is associated with lower travel time, especially in the analysis of credit.

scholarly journals Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors

2020 ◽  
Vol 29 (05) ◽  
pp. 2050005
Author(s):  
Lev V. Utkin ◽  
Mikhail V. Kots ◽  
Viacheslav S. Chukanov ◽  
Andrei V. Konstantinov ◽  
Anna A. Meldo
Keyword(s):  
Treatment Effects ◽  
Regression Function ◽  
Simple Modification ◽  
Conditional Average ◽  
Treatment Groups ◽  
Feature Vectors ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
The Difference ◽  
New Feature

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions.

scholarly journals Estimating Conditional Average Treatment Effects

2015 ◽  
Vol 33 (4) ◽  
pp. 485-505 ◽  
Author(s):  
Jason Abrevaya ◽  
Yu-Chin Hsu ◽  
Robert P. Lieli
Keyword(s):  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Treatment Interactions with Nonexperimental Data in Stata

2011 ◽  
Vol 11 (4) ◽  
pp. 545-555 ◽  
Author(s):  
Graham K. Brown ◽  
Thanos Mergoupis
Keyword(s):  
Experimental Data ◽  
Observational Data ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Average Treatment Effect ◽  
Interaction Parameters ◽  
Average Treatment ◽  
Interaction Terms ◽  
Treatment Interaction ◽  
Predicted Values

Treatment effects may vary with the observed characteristics of the treated, often with important implications. In the context of experimental data, a growing literature deals with the problem of specifying treatment interaction terms that most effectively capture this variation. Some results of this literature are now implemented in Stata. With nonexperimental (observational) data, and in particular when selection into treatment depends on unmeasured factors, treatment effects can be estimated using Stata's treatreg command. Though not originally designed for this purpose, treatreg can be used to consistently estimate treatment interaction parameters. With interactions, however, adjustments are required to generate predicted values and estimate the average treatment effect. In this article, we introduce commands that perform this adjustment for multiplicative interactions, and we show the required adjustment for more complicated interactions.

scholarly journals Bootstrap bias correction for average treatment effects with inverse propensity weights

2019 ◽  
Vol 52 (2) ◽  
pp. 187-200
Author(s):  
GUBHINDER KUNDHI ◽  
MARCEL VOIA
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Bias Correction ◽  
Treatment Effects ◽  
Potential Problem ◽  
Average Treatment Effect ◽  
Omitted Variables ◽  
Control Groups ◽  
Average Treatment ◽  
And Control

The estimated average treatment effect in observational studies is biased if the assumptions of ignorability and overlap are not satisfied. To deal with this potential problem when propensity score weights are used in the estimation of the treatment effects, in this paper we propose a bootstrap bias correction estimator for the average treatment effect (ATE) obtained with the inverse propensity score (BBC-IPS) estimator. We show in simulations that the BBC-IPC performs well when we have misspecifications of the propensity score (PS) due to: omitted variables (ignorability property may not be satisfied), overlap (imbalances in distribution between treatment and control groups) and confounding effects between observables and unobservables (endogeneity). Further refinements in bias reductions of the ATE estimates in smaller samples are attained by iterating the BBC-IPS estimator.

scholarly journals Combating Effort Avoidance in Computer Adaptive Practicing: Does a Problem Skipping Restriction Promote Learning?

2021 ◽  
Author(s):  
Alexander Olof Savi ◽  
Chris van Klaveren ◽  
Ilja Cornelisz
Keyword(s):  
Learning Outcomes ◽  
Primary Education ◽  
Large Scale ◽  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Different Types ◽  
Average Treatment Effects ◽  
Null Finding ◽  
Adaptive Practice

Effort is key in learning, evidenced by its omnipresence in both empirical findings and educational theories. At the same time, students are consistently found to avoid effort. In this study, we investigate whether limiting effort avoidance improves learning outcomes, and explore for whom this would be the case. In a large-scale computer adaptive practice system for primary education, over 150,000 participants were distributed across four conditions in which a problem-skipping option was delayed for 0, 3, 6, or 9 seconds. The results show that after a 14 week period, no average treatment effects in learning outcomes can be found between conditions. A substantive typology of students, based on the expected target mechanisms of the intervention, neither shows consistent conditional average treatment effects. Nevertheless, the substantive typology is shown to be meaningful, as the different types—toilers, skippers, and rushers—differ with respect to their learning outcomes. We argue that although the scale of the experiment suggests a precise null finding, the cumulative nature of the effect of problem skipping cautions against generalizing this finding to sustained intervention.

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