3. The Foundations of Causal Inference

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Covariance-Based Structural Equation Modeling in the Journal of Advertising: Review and Recommendations (2017, background): “Pearl (2009, 2010, 2012, 2014) provides a comprehensive review of causality and SEM, and points out that the greater accessibility to statistical techniques coincides with users who may lack a fundamental understanding of key principles.”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “For XAI, see [1, 2, 31, 55, 61, 67, 75, 111, 120, 128], for causal and counterfactual inference, see [63, 76, 131, 145, 146, 159, 185], and for optimization, see [32, 137, 166].”
Graphical Representation of Missing Data Problems (2015, methodology): “The bias-reducing properties of such proxy variables in the context of causal inference were discussed by Pearl (2010b).”
Why Propensity Scores Should Not Be Used for Matching (2019, methodology): “…Rubin, 1983) is the most common matching method, possibly even “the most developed and popular strategy for causal analysis in observational studies” (Pearl, 2010); it is used or referenced in over 47,300 scholarly articles.1 We show here that PSM, as it is most commonly used in practice or with…”
Climate-driven declines in arthropod abundance restructure a rainforest food web (2018, background): “Pearl J (2010) The foundations of causal inference.”