Minimal sufficient causation and directed acyclic graphs

By Tyler J. VanderWeele, James M. Robins. 2009

56 citations recorded by the source corpus.

Notions of minimal sufficient causation are incorporated within the directed acyclic graph causal framework. Doing so allows for the graphical representation of sufficient causes and minimal sufficient causes on causal directed acyclic graphs while maintaining all of the properties of causal directed acyclic graphs. This in turn provides a clear theoretical link between two major conceptualizations of causality: one counterfactual-based and the other based on a more mechanistic understanding of causation. The theory developed can be used to draw conclusions about the sign of the conditional covariances among variables.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, History and Philosophy of Science

View Paper: https://www.semanticscholar.org/paper/b37226c5ec66a632933cd6b7cff105d1ff50c16b

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In Artificial Intelligence

In Developmental and Educational Psychology

In History and Philosophy of Science

Citations in the corpus

Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies (2011, background): “Another possibility is to identify particular components of a treatment that are capable of affecting an outcome, rather than focusing on causal processes (e.g., VanderWeele and Robins 2009).”
Experimental designs for identifying causal mechanisms (2013, background): “We also discuss an alternative definition of causal mechanisms which focuses on causal components instead of processes (e.g. Rothman, 1976; VanderWeele and Robins, 2009), as well as other related quantities that have appeared in recent works (Geneletti, 2007; Spencer et al., 2005).”
Signed directed acyclic graphs for causal inference (2010, background): “In related work ( VanderWeele and Robins, 2007, 2009b ) we have derived rules governing the sign of conditional covariances in the presence of Signed Graphs for Causal Inference 121”