A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect

By James M. Robins. 1986

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In Causal Inference & Discovery

Citations in the corpus

Causal Reasoning for Algorithmic Fairness (2018, methodology): “In particular, in this paper we will make use primarily of the structural causal model (SCM) framework advocated by [29], which shares much in common with the approaches by [33] and [39].”
Fair Inference on Outcomes (2017, methodology): “f consistency and conditional ignorability, described in more detail in the next section, the expected counterfactual response E[Y (a)]can be estimated fromobserveddata using the parametric g-formula [20]: E[Y (a)]=P n E[Y | A =a,xi X\{A} ;αb] , (1) where P n is the empirical average function. In ”
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “The increasing availability of observational data from electronic health records makes it possible to learn individualized treatment responses from longitudinal disease trajectories containing information about patient covariates and treatments assignments over time (Robins, 1986; Robins et al., 200”
Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “) Although other formulations may be possible, also, we adopt the counterfactual language that was introduced by Richardson and Robins (2013) who referred to finest fully randomized causally interpretable structured tree graphs (Robins, 1986).”
External Validity: From Do-Calculus to Transportability Across Populations (2014, methodology): “Other approaches include, for example, potential-outcomes (Rubin (1974)), Structured Tree Graphs (Robins (1986)), decision analytic (Dawid (2002)), Causal Bayesian Networks (Spirtes, Glymour and Scheines (2000); Pearl (2000), Chapter 1; Bareinboim, Brito and Pearl (2012)), and Settable Systems…”
Doubly Robust Policy Evaluation and Optimization (2014, background): “Causal inference techniques have been studied to estimate the mean response of a given DTR (e.g., Robins, 1986; Murphy, van der Laan and Robins, 2001), and to optimize DTR (e.g., Murphy, 2003; Orellana, Rotnitzky and Robins, 2010).”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “Standard methods for estimating the effects of time-varying exposures were ﬁrst developed in the epidemiology literature and include the g-computation formula, Structural Nested Models and Marginal Structural Models (MSMs) (Robins, 1986; 1994; Robins et al., 2000; Robins & Hernán, 2008).”
Learning When-to-Treat Policies (2019, methodology): “The variant of fitted-Q iteration we implement follows the batch Q-learning algorithm as described in Murphy (2005) for solving the optimal Q function at each timestep: At each t = T, T − 1, . . . , 1, we solve14 Q̂∗t (·, ·) = argminQt 1 n n∑ i=1 ( max at+1 Q̂∗t+1(S (i) 1:(t+1), {A(i)1:t , at+1}) − ”
Causal inference in statistics: An overview (2009, background): “Weeding out myths and misconceptions from outdated traditions (Meek and Glymour, 1994; Greenland et al., 1999; Cole and Hernán, 2002; Arah, 2008; Shrier, 2009; Pearl, 2009b).”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “This is also often called the G-formula in the causal inference literature (Hernan and Robins, 2019; Robins, 1986).”
Counterfactual Graphical Models for Longitudinal Mediation Analysis With Unobserved Confounding (2012, methodology): “…equation approach to mediation analysis is a special case of a more general framework based on potential outcome counterfactuals, developed by Neyman (1923) and Rubin (1974), and extended and linked to non-parametric structural equations and graphical models by J. Robins (1986), and Pearl (2000).”
Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories (2012, background): “Optimal control problems arise in many real-life applications, such as engineering (Ried-miller 2005), medicine (Robins 1986; Murphy et al. 2001; Murphy 2003) or artiﬁcial intelligence (Sutton and Barto 1998).”
Causal Structure Learning (2017, background): “The resulting factorization is known as the g-formula (Robins, 1986), the manipulated density (Spirtes et al., 2000), or the truncated factorization formula (Pearl, 2009).”
Constructing Dynamic Treatment Regimes in Infinite-Horizon Settings (2014, unknown): “Potential outcome models are introduced by Neyman (1990) and Rubin (1978) for time-independent treatment and later extended by Robins (1986, 1987) to assess the time-dependent treatment effect from experimental and observational longitudinal studies.”
Causal analysis with Chain Event Graphs (2010, background): “Trees also have their advocates in the study of causality [27,30,25].”
The Book of Why: The New Science of Cause and Effect (2020, background): “ive causal models and explain why scientists might prefer them. These approaches differ from Pearl’s model in that they impose different amounts or different types of structure on the causal problem. Robins (1986) developed a causal model that is closely related to Pearl’s model and predates it. Bot”