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

In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

The seven tools of causal inference, with reflections on machine learning Page 1 54
COMMUNICATIONS OF THE ACM | MARCH 2019 | VOL. 62 | NO. 3 contributed articles ILL US …

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 …

Classical supervised learning produces unreliable models when training and target
distributions differ, with most existing solutions requiring samples from the target domain. We …

We study counterfactual identifiability in causal models with bijective generation
mechanisms (BGM), a class that generalizes several widely-used causal models in the …

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

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 …

Identifying causal effects from observational data is a pervasive challenge found throughout
the empirical sciences. Very general methods have been developed to decide the …

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 …