Drawbacks of ignoring the causal mechanisms when performing imitation learning have
recently been acknowledged. Several approaches both to assess the feasibility of imitation …

Additive noise models (ANMs) are an important setting studied in causal inference. Most of
the existing works on ANMs assume causal sufficiency, ie, there are no unobserved …

Causal identification is at the core of the causal inference literature, where complete
algorithms have been proposed to identify causal queries of interest. The validity of these …

The renowned difference-in-differences (DiD) estimator relies on the assumption of'parallel
trends,'which may not hold in many practical applications. To address this issue, economists …

Identifying causal effects is a key problem of interest across many disciplines. The two long-
standing approaches to estimate causal effects are observational and experimental …

We look at the problem of learning causal structure for a fixed downstream causal effect
optimization task. In contrast to previous work which often focuses on running interventional …

Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects
from observational data. When such an effect is not identifiable, it is necessary to perform a …

Context: Epidemiology critically depends on understanding causal relationships to
effectively address public health challenges. Theoretical advancements, such as those …