The Statistics of Causal Inference: A View from Political Methodology

By Luke Keele. Political Analysis, 2015

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Why Experimenters Might Not Always Want to Randomize, and What They Could Do Instead (2016, background): “Our recommendation not to randomize raises the question of identification, cf. the review in Keele (2015).”
The causal interpretation of estimated associations in regression models (2019, background): “See Keele (2015) for a detailed overview of different identification strategies.”
Unbiased Scene Graph Generation From Biased Training (2020, unknown): “It is also known as effect analysis [57, 41], which is widely adopted in medical, political or psychological research [45, 19, 8, 33, 21] as the tool of studying the effect of certain treatments or policies.”
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (2020, unknown): “Causal inference [23, 35] has been widely adopted in psychology, politics and epidemiology for years [36, 37, 38].”