Interpretation and identification of causal mediation.

By Judea Pearl. 2014

542 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability

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Citations in the corpus

Distilling Causal Effect of Data in Class-Incremental Learning (2021, methodology): “Based on the graph, we can formulate the causal effect between variables by causal intervention, which is denoted as the do(·) operation [30, 31].”
Visual Commonsense R-CNN (2020, background): “There has been a growing amount of efforts in marrying complementary strengths of deep learning and causal reasoning [49, 48] and have been explored in several contexts, including image classification [8, 40], reinforcement learning [46, 11, 5] and adversarial learning [28, 26].”
Empathetic Leadership: How Leader Emotional Support and Understanding Influences Follower Performance (2018, background): “The model presents a causal statement in line with Pearl (2012, 2014), Bollen and Pearl’s (2013), and Goldthorpe’s (2001) suggestions for establishing causal linkages between constructs.”
Causal Intervention for Weakly-Supervised Semantic Segmentation (2020, methodology): “CONTA is based on the backdoor adjustment [37] for P (Y ∣do(X)).”
When Celebrities Count: Power Distance Beliefs and Celebrity Endorsements (2018, background): “Because we measure the mediators after exposing participants to the experimental stimuli, the mediators should be endogenous; this endogeneity results in bias for the relationship between the mediator and the outcome variables (Pearl 2014).”
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.”
Do Not Cross Me: Optimizing the Use of Cross-Sectional Designs (2019, background): “The notion that the observed connection between X and Y might be attributable to additional causal variables has been discussed in other contexts such as endogeneity (Antonakis, Bendahan, Jacquart, & Lalive, 2010) and causal mediation (Imai, Keele, & Tingley, 2010; Pearl, 2014).”
The Mediation Myth (2015, methodology): “…or potential outcomes model (Rubin, 2005), principal stratification with propensity scores (Jo, Stuart, MacKinnon, & Vinokur, 2011), instrumental variable methods (Angrist, Imbens, & Rubin, 1996), conditional process modeling (Hayes, 2013), and causal mediation analysis (Pearl, 2014).”
iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering (2020, unknown): “reasoning [43, 42].”
TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, unknown): “Finally, mediation and moderation effects in nonlinear parametric systems (Pearl, 2014) and attribution problems in monotonic systems (Pearl, 2009a, Ch.”