Manyimportant questions posed in the natural and social sciences are causal in nature:What are the long-term effects of mild Covid-19 infection on lung and brain functions?

Second, they lack a specific focus on selecting a robust CA TE estimator.

Off-policy estimation: Oftentimes, one needs to estimate the linear functional based on observational data collected from a behavior policy.

In various domains, different subjects may exhibit different responses to the same set of treatments. The exploration of this heterogeneity in the effects resulting from exposure has gained substantial interest in recent years. For instance, inferring the heterogeneous effect of a medical treatment on clinical outcome can contribute to the development of personalized treatment (Cai et al., 2011). A similar concept has found application in personalized marketing as well (Chandra et al., 2022).

Accurately predicting conditional average treatment effects (CA TEs) is crucial in personalized medicine and digital platform analytics.