Bayesian Inference for Causal Effects: The Role of Randomization

By Donald B. Rubin. 1978

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “However, these works are based on Pearl’s causal framework (Pearl, 2009) and use structural equation models, while our method deconfounds the estimation of treatment effects in the potential outcomes framework (Neyman, 1923; Rubin, 1978; Robins & Hernán, 2008).”
The Econometrics of Randomized Experiments (2016, methodology): “The work by Rubin (1973, 1975, 1978) led Holland (1986) [8] to label it the Rubin Causal Model (RCM).”
Recursive partitioning for heterogeneous causal effects (2015, background): “We assume that observations are exchangeable, and that there is no interference [the stable unit treatment value assumption (10)].”
Counterfactual Estimation and Optimization of Click Metrics for Search Engines (2014, background): “lection, which has been known to be a critical condition for drawing valid causal conclusions [27].”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “We adopt the potential outcomes framework proposed by (Neyman, 1923; Rubin, 1978) and extended by (Robins & Hernán, 2008) to account for time-varying treatments.”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, background): “Throughout this article, we will assume that the response mechanism is unconfounded in the sense that yi and ti are conditionally independent given xi (Rubin, 1978); this assumption has also been called strong ignorability (Rosenbaum and Rubin, 1983).”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, background): “Throughout this article, we will assume that the response mechanism is unconfounded in the sense that yi and ti are conditionally independent given xi (Rubin, 1978); this assumption has also been called strong ignorability (Rosenbaum and Rubin, 1983).”
Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition (2017, background): “Individual-level causal effects are defined as the difference between these “potential outcomes,” for example, Yi(1) − Yi(0) (Rubin, 1978).”
A Potential Outcomes View of Value-Added Assessment in Education (2004, methodology): “In this discussion we approach value-added assessment from a “potential outcomes” (Rubin Causal Model, RCM) point of view (Rubin 1974, 1978, 2003; Holland 1986; Little and Rubin 2001), with the goal of clarifying the estimation goals and understanding the limitations of data for the types of…”
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, background): “This condition is also referred to as conditional ignorability or unconfoundedness [27, 18]: there exists no unobserved confounders (UCs) that simultaneously affects the treatment at any stage and all the subsequent outcomes given a set of observed covariates.”
BAYESIAN ASPECTS OF TREATMENT CHOICE (2011, background): “Rubin (1978) discusses the role of randomization in Bayesian inference for causal effects.”
How much green for the buck? Estimating additional and windfall effects of French agro-environmental schemes by DID-matching (2010, background): “16 As suggested by Rosenbaum and Rubin [28], we use standardized differences to assess the quality of our adjustment.”
Data-adaptive selection of the truncation level for Inverse-Probability-of-Treatment-Weighted estimators (2008, unknown): “The data we analyze were collected as part of a community-based longitudinal study of physical activity and fitness (Study of Physical Performance and Age Related Changes in Sonomans - SPPARCS), in which Tager et al. (1998) followed a group of people aged 55 years and older living in and around Sono”