Controlling Bias in Observational Studies: A Review.

By W. G. Cochran, Donald B. Rubin. 1974

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Citations in the corpus

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “A natural approach to observational studies is the matching of treated and control units such that underlying background covariates are balanced (Chapin, 1947; Greenwood, 1945).”
Estimating causal effects of treatments in randomized and nonrandomized studies. (1974, methodology): “Recent work on methods of reducing bias in nonrandomized studies is summarized in Cochran and Rubin (1974).”
Dealing with limited overlap in estimation of average treatment effects (2009, unknown): “Cochran and Rubin (1973) suggest using caliper matching where units whose match quality is too low according to the distance in terms of the propensity score are left unmatched.”
Comparing UK Tax Returns of Foreign Multinationals to Matched Domestic Firms (2019, unknown): “William G. Cochran (1973) and Rosenbaum and Rubin (1985) suggest using a caliper width that is a proportion of the standard deviation of the logit of the propensity score, specifically 0.2 of standard deviation was suggested to eliminate approximately 99% of the bias due to the measured confounders.”
Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies (2010, unknown): “In a subsequent article, Rosenbaum and Rubin examined the construction of a control group using matching on the propensity score [4].”
Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments (2007, unknown): “A version of nearest neighbor matching, called “caliper” matching [Cochran and Rubin (1973)], makes matches to person i only if”
The use of propensity score matching in the evaluation of active labour market policies (2002, methodology): “A variant on this nearest-neighbour matching is caliper matching (Cochrane & Rubin, 1973).”
The minimum wage and the Great Recession: Evidence of effects on the employment and income trajectories of low-skilled workers (2014, methodology): “21This approach to sample selection is sometimes called the “caliper” method (Cochran and Rubin, 1973; Crump, Hotz, Imbens, and Mitnik, 2006).”
Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign (2009, methodology): “An old technique to adjust for possible confounding from a single continuous variable is to match within a caliper on it (Cochran and Rubin 1973), that is to match treatment and control units subject to the restriction that matched units differ by no more than a fixed constant c on that variable.”
Standardizing fishery-dependent catch and effort data in complex fisheries with technology change (2006, background): “As a literature developed on ‘‘natural experiments’’ and observational studies (Wold 1956; Leamer 1978, in econometrics; Cochran and Rubin 1973; Cochran 1983, in social policy and public health programs; a review by McKinlay 1975; Diamond 1986, and Hairston 1989, in ecology), a great deal of…”
Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology (2006, background): “This approach has been known for many years to fail in general (e.g., Cochran and Rubin, 1973; LaLonde, 1986).”
A Review of Domain Adaptation without Target Labels (2019, background): “Corrections are based on estimating - or in cases where there is control of the experimental design, knowing - the probability that an instance will be selected for observation [3], [4], [5].”