• Corpus ID: 238259023

Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments

  title={Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments},
  author={Phillip Heiler and Michael C. Knaus},
Binary treatments in empirical practice are often (i) ex-post aggregates of multiple treatments or (ii) can be disaggregated into multiple treatment versions after assignment. In such cases it is unclear whether estimated heterogeneous effects are driven by effect heterogeneity or by treatment heterogeneity. This paper provides estimands to decompose canonical effect heterogeneity into the effect heterogeneity driven by different responses to underlying multiple treatments and potentially… 

Figures and Tables from this paper

Double Machine Learning Based Program Evaluation under Unconfoundedness
  • M. Knaus
  • Economics, Computer Science
  • 2020
This paper consolidates recent methodological developments based on Double Machine Learning with a focus on program evaluation under unconfoundedness and finds evidence that estimates of individualized heterogeneous effects can become unstable.


Recursive partitioning for heterogeneous causal effects
  • S. Athey, G. Imbens
  • Mathematics, Economics
    Proceedings of the National Academy of Sciences
  • 2016
This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
  • Stefan Wager, S. Athey
  • Computer Science, Mathematics
    Journal of the American Statistical Association
  • 2018
This is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference and is found to be substantially more powerful than classical methods based on nearest-neighbor matching.
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a
Quasi-oracle estimation of heterogeneous treatment effects
This paper develops a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies that have a quasi-oracle property, and implements variants of this approach based on penalized regression, kernel ridge regression, and boosting, and find promising performance relative to existing baselines.
Estimating Conditional Average Treatment Effects
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
This work theoretically analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression and highlights how this theoretical reasoning can be used to guide principled algorithm design and translate into practice by considering a variety of neural network architectures as base-learners for the discussed metalearning strategies.
Causal inference under multiple versions of treatment
The potential outcomes framework, as articulated by Rubin, makes an assumption of no multiple versions of treatment, and an extension of this possible outcomes framework is discussed to accommodate causal inference under violations of this assumption.
Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap
Estimators of average treatment effects under unconfounded treatment assignment are known to become rather imprecise if there is limited overlap in the covariate distributions between the treatment