Estimation of the causal effects of time-varying exposures

By James M. Robins, Miguel A. Hern'an. 2008

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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).”
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, methodology): “Illustrative applications of the general approach are provided in [6, 16], and summaries of g-computation (and other “g-methods” for estimating time-varying treatment effects) can be found in [2, 3].”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “The standard assumptions needed for identifying the treatment effects are (Robins & Hernán, 2008; Lim et al., 2018; Schulam & Saria, 2017): Assumption 1: Consistency .”
TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES (2018, methodology): “To connect the counter-factual outcomes with the observed data, we make the following three standard assumptions [Murphy, van der Laan and Robins (2001), Robins and Hernán (2009), Orellana, Rotnitzky and Robins (2010)].”
Observational data for comparative effectiveness research: an emulation of randomised trials to estimate the effect of statins on primary prevention of coronary heart disease (2011, methodology): “Informally, the IP weight for each eligible individual at a given time is the inverse (reciprocal) of her probability of having received the treatment history that she actually received.(22,23) For example, suppose an individual received statins during the first month of follow-up but not during the”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “For a basic description of these methods, see Hernán and Robins (14).”
Handbook of Big Data (2016, background): “This challenge is generally known as time-dependent confounding [27,31,48]: time-varying covariates confound the effect of future exposures on the outcome, but are affected by past exposures.”
From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, unknown): “By learning based on how other patients have responded to the treatment, these methods can be trained to estimate potential outcomes (i.e., patient outcomes under different treatment options) 26–28 conditioned on the patient’s clinical state.”
Semiparametric doubly robust targeted double machine learning: a review (2022, methodology): “The von Mises terminology comes from, e.g., von Mises [1947], and has been used by Fernholz [1983], van der Vaart [2000], Robins et al. [2009], Kandasamy et al. [2015], among others.”
On causal inference in the presence of interference (2010, background): “Despite the availability under Assumptions 1 and 2, of reasonable variance estimators given by Equations (6)–(9) for the various estimators of causal effects proposed by Hudgens and Halloran,6 a formal framework for statistical inference on population average causal effects is currently lacking.”
Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models (2013, background): “However, in practice, any unsaturated models may be misspecified, and thus may lead to biased causal effect estimates (Robins and Hernán, 2008).”
Bootstrap Inference When Using Multiple Imputation (2016, unknown): “affected by prior treatment using g-methods [31, 26].”
On the Differences between General Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model: Interpretation of Cross-Lagged Parameters and Model Choice (2020, methodology): “For this reason, efforts by researchers to devise a better methodology are continuing (e.g., Asparouhov et al., 2018; Hamaker et al., 2015; Imai & Ratkovic, 2015; Robins, 1999; Robins & Hernán, 2009; Zyphur et al., 2020a).”