Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …

The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …

Cause-and-effect relationships play a central role in how we perceive and make sense of
the world around us, how we act upon it, and ultimately, how we under stand ourselves …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

One of the central elements of any causal inference is an object called structural causal
model (SCM), which represents a collection of mechanisms and exogenous sources of …

This paper investigates the problem of bounding counterfactual queries from an arbitrary
collection of observational and experimental distributions and qualitative knowledge about …

Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …

Some of the most prominent results in causal inference have been developed in the context
of atomic interventions, following the semantics of the do-operator and the inferential power …

Learning about cause and effect is arguably the main goal in applied econometrics. In
practice, the validity of these causal inferences is contingent on a number of critical …

Causal contributions measure the strengths of different causes to a target quantity.
Understanding causal contributions is important in empirical sciences and data-driven …