Linear Models: A Useful “Microscope” for Causal Analysis
By Judea Pearl. 2013
156 citations recorded by the source corpus.
Abstract This note reviews basic techniques of linear path analysis and demonstrates, using simple examples, how causal phenomena of non-trivial character can be understood, exemplified and analyzed using diagrams and a few algebraic steps. The techniques allow for swift assessment of how various features of the model impact the phenomenon under investigation. This includes: Simpson’s paradox, case–control bias, selection bias, missing data, collider bias, reverse regression, bias amplification, near instruments, and measurement errors.
Subfields: Statistics and Probability
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In Statistics and Probability
- Elias Bareinboim
- Jin Tian
- Judea Pearl
- Rogier A. Kievit
- Lourens J. Waldorp
- Denny Borsboom
- Willem E. Frankenhuis
- Tyler J. VanderWeele
Citations in the corpus
- Recovering from Selection Bias in Causal and Statistical Inference (2014, methodology): “339-341) and (Pearl 2013) for further explanations of this bias mechanism.”
- UvA-DARE ( Digital Academic Repository ) Simpson ' s paradox in psychological science : a practical guide (2017, background): “Simpson’s paradox is conceptually and analytically related to many statistical challenges and techniques, including causal inference (Pearl, 2000, 2013), the ecological fallacy (Robinson, 1950; Kramer, 1983; King, 1997; King and Roberts, 2012), Lord’s paradox, (Tu et al., 2008), propensity score…”
- TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS (2014, unknown): “Anecdotically, none of the six textbooks surveyed in Chen and Pearl (2013) explains to readers what justification there is for excluding variables from an equation; such explanations require that equations be given causal interpretation, which textbooks are reluctant to do.”
- Interpretation and identification of causal mediation. (2014, unknown): “12 (See Bollen & Pearl, 2013, for further discussion of this important observation, which is often overlooked in the potential-outcome literature; e.g., Rubin, 2010; Sobel, 2008.)”