Conditional Average Treatment Effects and Decision Making

2014 ◽  
Author(s):  
Mario Samano
Keyword(s):  
Decision Making ◽  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects

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 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.

scholarly journals Estimating Conditional Average Treatment Effects

2014 ◽  
pp. 00-00 ◽  
Author(s):  
Jason Abrevaya ◽  
Yu-Chin Hsu ◽  
Robert P. Lieli
Keyword(s):  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data

2020 ◽  
pp. 1-18
Author(s):  
Steve Yadlowsky ◽  
Fabio Pellegrini ◽  
Federica Lionetto ◽  
Stefan Braune ◽  
Lu Tian
Keyword(s):  
Observational Data ◽  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects

scholarly journals Estimation of Conditional Average Treatment Effects With High-Dimensional Data

2020 ◽  
pp. 1-15 ◽  
Author(s):  
Qingliang Fan ◽  
Yu-Chin Hsu ◽  
Robert P. Lieli ◽  
Yichong Zhang
Keyword(s):  
Treatment Effects ◽  
High Dimensional Data ◽  
High Dimensional ◽  
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.

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