In an influential article published in 1982, Bas Van Fraassen developed an argument against causal realism on the basis of an analysis of the Einstein-Podolsky-Rosen correlations of quantum mechanics. Several philosophers of science and experts in causal inference – including some causal realists like Wesley Salmon – have accepted Van Fraassen’s argument, interpreting it as a proof that the qua...

To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, ...

Existing score-based causal model search algorithms such as GES (and a speeded up version, FGS) are asymptotically correct, fast, and reliable, but make the unrealistic assumption that the true causal graph does not contain any unmeasured confounders. There are several constraint-based causal search algorithms (e.g RFCI, FCI, or FCI+) that are asymptotically correct without assuming that there ...

Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become crucial part Artificial Intelligence community, causality can be key tool for overcoming some limitations correlation-based Machine Learning systems. research generally div...

Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal inference, is the ‘flip side’ of an important metaphysical fact about causation—that causes can be used to manipulate their effects. This paper disagrees. First, the premise of their proof does not demand that causes can be used to manipulate their effects but rather ...

This article considers the policy of retaining low-achieving children in kindergarten rather than promoting them to first grade. Under the stable-unit-treatment-value assumption (SUTVA) as articulated by Rubin, each child at risk of retention has two potential outcomes: Y(1) if retained and Y(0) if promoted. However, SUTVA is questionable because a child’s potential outcomes will plausibly depe...

Computational models of analogy have assumed that the strength of an inductive inference about the target is based directly on similarity of the analogs, and in particular on shared higher-order relations. However, in Experiment 1 we show that reducing analogical overlap by eliminating a higher-order causal relation (a preventive cause present in the source) from the target increased inductive ...