Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"

TL;DR

This paper discusses the importance of estimating and making inferences about treatment heterogeneity in randomized experiments, emphasizing the potential of proxy-based summary features and sample splitting, while acknowledging ongoing challenges and alternative methods.

Contribution

The paper highlights the significance of targeting summary features of the CATE using proxy estimation with sample splitting and encourages further research on inference methods.

Findings

Recognition of the importance of treatment heterogeneity analysis.

Potential of proxy estimation and sample splitting for inference.

Call for continued research on inference challenges and alternative approaches.

Abstract

We warmly thank Kosuke Imai, Michael Lingzhi Li, and Stefan Wager for their gracious and insightful comments. We are particularly encouraged that both pieces recognize the importance of the research agenda the lecture laid out, which we see as critical for applied researchers. It is also great to see that both underscore the potential of the basic approach we propose - targeting summary features of the CATE after proxy estimation with sample splitting. We are also happy that both papers push us (and the reader) to continue thinking about the inference problem associated with sample splitting. We recognize that our current paper is only scratching the surface of this interesting agenda. Our proposal is certainly not the only option, and it is exciting that both papers provide and assess alternatives. Hopefully, this will generate even more work in this area.