Using representation balancing to learn conditional-average dose responses from clustered data
This addresses a specific challenge in causal inference for domains like healthcare and economics, but it is incremental as it builds on existing CADR estimators by handling clustered data.
The paper tackled the problem of estimating conditional average dose responses (CADR) from clustered observational data where doses are assigned to different population segments, proposing CBRNet to learn dose-agnostic covariate representations through representation balancing for unbiased inference.
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.