Partial Identification of Dose Responses with Hidden Confounders
This work addresses a critical gap in causal inference for policy- and decision-makers by providing sensitivity analysis for continuous treatments, though it is incremental as it builds on existing sensitivity analysis frameworks.
The paper tackles the problem of inferring causal effects of continuous-valued treatments from observational data when hidden confounders prevent point identification, by presenting a novel methodology to bound these effects; results show tighter coverage of the true dose-response curve compared to existing methods in semi-synthetic benchmarks.
Inferring causal effects of continuous-valued treatments from observational data is a crucial task promising to better inform policy- and decision-makers. A critical assumption needed to identify these effects is that all confounding variables -- causal parents of both the treatment and the outcome -- are included as covariates. Unfortunately, given observational data alone, we cannot know with certainty that this criterion is satisfied. Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden. While much attention is focused on sensitivity analyses for discrete-valued treatments, much less is paid to continuous-valued treatments. We present novel methodology to bound both average and conditional average continuous-valued treatment-effect estimates when they cannot be point identified due to hidden confounding. A semi-synthetic benchmark on multiple datasets shows our method giving tighter coverage of the true dose-response curve than a recently proposed continuous sensitivity model and baselines. Finally, we apply our method to a real-world observational case study to demonstrate the value of identifying dose-dependent causal effects.