Coupling Generative Modeling and an Autoencoder with the Causal Bridge
This addresses causal inference challenges in fields like healthcare or economics where hidden confounders bias results, offering an incremental improvement over existing proxy-based techniques.
The paper tackles the problem of inferring causal treatment effects when unobserved confounders exist, by proposing a method that couples the causal bridge with an autoencoder to improve estimates, showing effectiveness on synthetic and real-world data compared to state-of-the-art proxy methods.
We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate sets of control (proxy) measurements associated with treatment and outcomes, which are used to estimate treatment effects through a function termed the em causal bridge (CB). We present a new theoretical perspective, associated assumptions for when estimating treatment effects with the CB is feasible, and a bound on the average error of the treatment effect when the CB assumptions are violated. From this new perspective, we then demonstrate how coupling the CB with an autoencoder architecture allows for the sharing of statistical strength between observed quantities (proxies, treatment, and outcomes), thus improving the quality of the CB estimates. Experiments on synthetic and real-world data demonstrate the effectiveness of the proposed approach in relation to the state-of-the-art methodology for proxy measurements.