The Role of the Propensity Score in Estimating Dose-Response Functions

By Guido W. Imbens. 2000

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Citations in the corpus

Balanced Policy Evaluation and Learning (2017, background): “Noting that [17, 18] SAPE(⇡) = E[ 1 n Pn i=1 Yi ⇥ ⇡Ti(Xi)/'Ti(Xi) | X1:n], one first fits a probabilistic classification model '̂ to {(Xi, Ti) : i 2 [n]} and then estimates SAPE in an alternate but also plug-in fashion: ⌧̂ IPW(⇡) = ⌧̂W IPW(⇡), W IPW i (⇡) = ⇡Ti(Xi)/'̂Ti(Xi) For a deterministic polic”
The State of Applied Econometrics - Causality and Policy Evaluation (2016, positive): “Covariates typically play only a minor role in the primary analyses there, although they can improve precision (Imbens and Lemieux [2008], Calonico et al.”
Estimation of Multivalued Treatment Effects under Conditional Independence (2013, methodology): “Imbens (2000) provides an extensive discussion on identification of multivalued mean treatment effects under ignorability (see also Hirano and Imbens (2004)).”
The Effects of Inquiry Teaching on Student Science Achievement and Attitudes: Evidence from Propensity Score Analysis of PISA Data (2015, methodology): “Because the treatment is a five-level categorical variable, the generalized propensity scores were estimated with multinomial logistic regression (Imbens, 2000; Lechner, 2001).”
Sequential Matching Estimation of Dynamic Causal Models (2004, positive): “The only difference is that it is of the multiple treatment type, because four different sequences are involved (cf. Imbens, 2000, Lechner, 2001a).”
Estimation of causal effects with multiple treatments: a review and new ideas (2017, methodology): “For nominal treatment, the multinomial logistic and the multinomial probit models have been proposed to estimate R(X), and for ordinal treatment, the proportional odds model has been suggested (Imbens, 2000; Imai and Van Dyk, 2004).”
Opiates for the Matches: Matching Methods for Causal Inference (2009, methodology): “For an excellent review of recent developments in methods for program evaluation see Imbens and Wooldridge (2008). For additional reviews of the matching literature see Morgan and Harding (2006), Morgan and Winship (2007), Rosenbaum (2005), and Rubin (2006).”
Programme Evaluation with Multiple Treatments (2004, background): “…with respect to X, propensity score matching can often be quite useful since ‘in practice inference for average treatment effects is often less sensitive to misspecification of the propensity score than to specifications of the conditional expectation of the potential outcomes’ (Imbens, 2000).”
Why Propensity Scores Should Not Be Used for Matching (2019, background): “Identifying assumptions and, conditional on the choice of an outcome model applied to raw or matched data, optimal estimation procedures are well known (Lechner, 2001; Imbens, 2000; Iacus, King and Porro, 2015).”
What Do Social Scientists Know About the Benefits of Marriage? A Review of Quantitative Methodologies (2004, positive): “Imbens (2000) and Lechner (2001) have recently extended propensity score matching methods to examine differences in outcomes across multiple groups.”
Social Influence Given (Partially) Deliberate Matching: Career Imprints in the Creation of Academic Entrepreneurs1 (2017, methodology): “Third, after generating estimates of the probability that protégés match to specific advisers, we then employ a variant of propensity score estimation (Imbens 2000) to assess the post-match effect of advisers’ commercial orientation on Scholar patenting.”
Additionality or crowding-out? An overall evaluation of public R&D subsidy on private R&D expenditure (2016, methodology): “This estimation strategy is based on the key assumption, weak unconfoundedness, formulated by Imbens (2000), which requires only the pairwise independence of the treatment with each (not joint) of the potential outcomes.”
Beyond the Methodological Gold Standards of Behavioral Research: Considerations for Practice and Policy (2004, background): “…variables estimation (Davidson & MacKinnon, 1993; Foster & McLanahan, 1996; Yoshikawa, Rosman, & Hsueh, 2001) and propensity scoring (Foster, 2003; Imbens, 2000; Rosenbaum & Rubin, 1983, 1985) that can model selection factors relating to program participation and unmeasured confounding.”