"This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining bias-free effect estimates in such models."

"Haavelmo was the first to recognize the capacity of economic models to guide poli- cies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection."

Uncle Judea sounds a bit testy in this one, but no doubt anyone would be if they had to keep swatting down such pathetic misunderstandings passing for objections.

Those would be _instrumental_ variables. Implications for the collected scholarly works of S. Levitt left as an exercise for the reader.

Described by Uncle Judea as "A new survey paper, gently summarizing everything I know about causation (in only 43 pages)".

Pearl vs. Rubin: "the propensity score is a probabilistic, not a causal concept. Therefore, in the limit of very large sample, PS methods are bound to produce the same bias as straight stratification on the same set of measured covariates. They merely offer an effective way of approaching the asymptotic estimate which, due to the high dimensionality of X, is practically unattainable with straight stratification. Still, the asymptotic estimate is the same in both cases, and may or may not be biased, depending on the set of covariates chosen."

Note the extreme weakness of the sense in which Pearl is even "half-Bayesian"; the blessed St. Jerzy could agree with it.