Causal Inference for Statistics, Social, and Biomedical Sciences: Sensitivity Analysis and Bounds

By Guido W. Imbens, Donald B. Rubin. 2015

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Citations in the corpus

Recommendations as Treatments: Debiasing Learning and Evaluation (2016, methodology): “This process is typically called the Assignment Mechanism in causal inference (Imbens & Rubin, 2015) or the Missing Data Mechanism in missing data analysis (Little & Rubin, 2002).”
Economic Analysis and Statistical Disclosure Limitation (2016, background): “As Imbens and Lemieux note, this is a Rubin Causal Model (Rubin 1974; Holland 1986; Imbens and Rubin 2015).”
Uniform, nonparametric, non-asymptotic confidence sequences (2018, methodology): “Hoeffding bound CLT bound Jamieson et al. (2013) Balsubramani (2014) Zhao et al.”
Confounding-Robust Policy Improvement (2018, background): “Some approaches from sensitivity analysis for assessing unconfoundedness require auxiliary data or additional structural assumptions, which we do not assume here (Imbens and Rubin 2015).”
Identification and Estimation of Spillover Effects in Randomized Experiments (2017, methodology): “As it is common in analysis of experiments (see e.g. Imbens and Rubin, 2015), potential outcomes are seen as fixed, and all the randomness comes through the treatment assignment mechanism.”
Covariate balancing propensity score by tailored loss functions (2016, methodology): “a specication search|the estimated propensity score is applicable only if it balances covariates well. A simple strategy is to gradually increase the model complexity by forward stepwise regression (Imbens and Rubin, 2015, Section 13.3{13.4), but as a numerical example below indicates, this has no g”
Sensitivity Analysis in Observational Research: Introducing the E-Value (2017, methodology): “, that some third factor related to both the treatment and the outcome might explain their association, with no true causal effect(12-22).”
Inference on breakdown frontiers (2017, background): “Their approach was substantially generalized by Rosenbaum and Rubin (1983), which is discussed in detail in Chapter 22 of Imbens and Rubin (2015).”
High-dimensional econometrics and regularized GMM (2018, background): “see [78], [76], [1], and [44], among many others.”
Unbiased Learning-to-Rank with Biased Feedback (2016, methodology): “Using this counterfactual modeling setup, we can get an unbiased estimate of ∆(y|xi, ri) for any new ranking y (typically different from the presented ranking ȳi) via the inverse propensity scoring (IPS) estimator [7, 19, 8]”