The Similarity of Causal Inference in Experimental and Non‐experimental Studies

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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning (2019, background): “In terms of changes to pixels, semantic concepts, and game state, we distinguish among three classes of interventions: distortion , semantics-preserving , and fat-hand (see (Scheines, 2005).”
The Relation between Kin and Multilevel Selection: An Approach Using Causal Graphs (2016, background): “21 This is related to what are called ‘fat hand’ interventions in the causal modelling literature; see (Scheines [2006]).”
Constitutive Relevance, Mutual Manipulability, and Fat-Handedness (2016, background): “11 A fat-handed intervention is an intervention that influences its ultimate effect along two (or more) different causal paths (Scheines [2005], p.”
Inferring Causality from Noninvasive Brain Stimulation in Cognitive Neuroscience (2020, methodology): “…note that, in the context of this paper, causal inference simply means “inferring causality” or “inferring that one variable is the cause of another” (Scheines, 2005), an inference that may either be based on the controlled randomized experiment or, under certain conditions, on observational…”