MULTIVARIATE MATCHING METHODS THAT ARE EQUAL PERCENT BIAS REDUCING, I: SOME EXAMPLES

By 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): “In the 1970’s and 1980’s, a large literature on different dimen-sion reduction approaches to matching was developed (e.g., Rubin, 1973a,b, 1976; Cochran and Rubin, 1973) with the extreme being work on propensity score matching, which was later extended to work on penalized regression approaches…”
Matching With Multiple Control Groups With Adjustment for Group Differences (2008, unknown): “…−XmcÞ+ dEðDt −DmcÞ = bðmt −EðXmcÞÞ+ dð0− Nt −mNt Þ = bmt − b mNt mc1 + m Nt p 4 sc1log Nc1 m + Nt −m Nt mt + Nt −mNt p 4 stlog Nt Nt −m + d m−Nt Nt : This formula uses the approximation for the tail expectation of a univariate standard normal distribution from Rubin (1976), (N, n)≈ p 4 lnðN n Þ.”
Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R (2008, unknown): “Then, as outlined in Rubin (1976a), a matching procedure is EPBR if E(X | T = 1)− Z = γ {E(X | T = 1)− E(X | T = 0)} for a scalar 0 ≤ γ ≤ 1.”
A Potential Outcomes View of Value-Added Assessment in Education (2004, methodology): “…if the missing data are missing completely at random (MCAR, Little & Rubin, 2002) and is not appropriate in settings such as this with longitudinal data and missing outcomes (see, e.g., Barnard et al., 1998, 2003), even when the missing data are ignorable (Rubin, 1976c; Little and Rubin, 2002).”
Propensity score matching strategies for evaluating substance abuse services for child welfare clients (2006, methodology): “This method was invented prior to the method of propensity score matching (Cochran & Rubin, 1973; Rubin, 1976, 1979, 1980).”
Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. (2000, background): “Simulations (59, 63, 75) and actual applications (10, 44, 69) indicate that combined strategies are generally superior to either strategy alone, for the goal of achieving essentially the same answer from an observational study as the parallel randomized experiment.”
Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence (2004, background): “This may be due to the equal percentage bias reduction of Mahalanobis metric (Rubin, 1976).”
Affinely invariant matching methods with discriminant mixtures of proportional ellipsoidally symmetric distributions (2006, methodology): “We use as a running example the estimation of the effects of smoking on lung cancer, where the results here were used to motivate diagnostics for the results of matching [15].”