Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models

By James M. Robins, Andrea Rotnitzky, D. Scharfstein. 2000

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In Statistics and Probability

Citations in the corpus

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “…to learn individualized treatment responses from longitudinal disease trajectories containing information about patient covariates and treatments assignments over time (Robins, 1986; Robins et al., 2000; Robins & Hernán, 2008; Soleimani et al., 2017; Schulam & Saria, 2017; Lim et al., 2018).”
Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “There is a large literature on testing the validity of the assumptions made in multiple imputation, as well as a variety of sensitivity analyses (Little and Rubin 1987; Robins et al. 1999; Scharfstein et al. 1999; Gelman et al. 2005; Carpenter et al. 2007; NAP 2010).”
Marginal Mean Models for Dynamic Regimes (2001, background): “Thus, an extension to this setting of the sensitivity analyses of Robins, Rotnitzky, and Scharfstein (1999b) and Scharfstein et al. (1999) is needed.”
Challenges of Using Text Classifiers for Causal Inference (2018, background): “Missing data is closely related to causal inference – both are interested in hypothetical distributions that we cannot directly observe (Robins et al., 2000; Shpitser et al., 2015).”
What-If Reasoning with Counterfactual Gaussian Processes (2017, methodology): “Formal procedures, such as sensitivity analyses (e.g., Robins et al. 2000, Scharfstein et al. 2014), that can identify when causal assumptions conﬂict with a data set will help to make our method more easily applied in practice.”
Towards Understanding Sycophancy in Language Models (2023, unknown): “This allows us to measure the sensitivity to unobserved factors, such as hidden confounders (Rosenbaum & Rubin, 1983; Robins et al., 2000).”
Sensitivity Analysis in Observational Research: Introducing the E-Value (2017, methodology): “Numerous sensitivity analysis techniques have been developed for different statistical models (14-22,24-40).”
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding (2019, background): “This basic idea has led to a large literature, see (Dorie et al., 2016; Robins et al., 2000) as examples among many of the existing state-of-the-art papers on this topic.”
Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns (2003, background): “Robins et al. (2000) showed that theorem 2 holds in model B(3).”