Using representation balancing to learn conditional-average dose responses from clustered data

TL;DR

This paper introduces CBRNet, a novel estimator that uses representation balancing to learn dose responses from clustered observational data, addressing challenges in causal inference across various domains.

Contribution

The paper proposes CBRNet, a new method that learns cluster-agnostic covariate representations for unbiased CADR estimation from clustered data, improving over existing approaches.

Findings

CBRNet outperforms state-of-the-art methods on a new benchmarking dataset.

Representation balancing effectively mitigates biases from clustered data.

Clustered data significantly impacts model performance, highlighting the need for cluster-agnostic approaches.

Abstract

Estimating a unit's responses to interventions with an associated dose, the "conditional average dose response" (CADR), is relevant in a variety of domains, from healthcare to business, economics, and beyond. Such a response typically needs to be estimated from observational data, which introduces several challenges. That is why the machine learning (ML) community has proposed several tailored CADR estimators. Yet, the proposal of most of these methods requires strong assumptions on the distribution of data and the assignment of interventions, which go beyond the standard assumptions in causal inference. Whereas previous works have so far focused on smooth shifts in covariate distributions across doses, in this work, we will study estimating CADR from clustered data and where different doses are assigned to different segments of a population. On a novel benchmarking dataset, we show the impacts of clustered data on model performance and propose an estimator, CBRNet, that learns cluster-agnostic and hence dose-agnostic covariate representations through representation balancing for unbiased CADR inference. We run extensive experiments to illustrate the workings of our method and compare it with the state of the art in ML for CADR estimation.