Causes of Effects and Effects of Causes

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Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms (2018, background): “ve causal DAG and exogenous noise u i: X = f(pa(X);u). Rea1The distinction is that p (Y a )jX reasons about the effects of causes while p(Y (a)jY;a0;X) reasons about the causes of effects (see, e.g., Pearl (2015)). Algorithm 1: Constructing a Stable Conditioning Set Input: Graph G, number of variabl”
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, background): “” For example, how likely it is that Joe’s swimming exercise was a necessary (or sufficient) cause of Joe’s death (Pearl, 2015a; Halpern and Pearl, 2005).”
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates (2017, background): “2016), although to do so is incorrect (Pearl 2015).”
The seven tools of causal inference, with reflections on machine learning (2019, background): “” For example, how likely it is that Joe’s swimming exercise was a necessary (or sufficient) cause of Joe’s death.(7,20) Tool 4.”