| In 1986, Robins published the paper "A New Approach to Causal Inference in Mortality Studies", which introduced a new framework for drawing causal inference from observational data. In this paper and in other articles published around the same time, Robins showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. This framework is mathematically very closely related to [[Judea Pearl]]'s graphical framework Non-Parametric Structural Equations Models, which Pearl developed independently shortly thereafter. Pearl's graphical models are a more restricted version of this theory.<ref name=":2">Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality https://csss.uw.edu/files/working-papers/2013/wp128.pdf</ref> | | In 1986, Robins published the paper "A New Approach to Causal Inference in Mortality Studies", which introduced a new framework for drawing causal inference from observational data. In this paper and in other articles published around the same time, Robins showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. This framework is mathematically very closely related to [[Judea Pearl]]'s graphical framework Non-Parametric Structural Equations Models, which Pearl developed independently shortly thereafter. Pearl's graphical models are a more restricted version of this theory.<ref name=":2">Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality https://csss.uw.edu/files/working-papers/2013/wp128.pdf</ref> |
| In his original paper on causal inference, Robins described two new methods for controlling for confounding bias, which can be applied in the generalized setting of time-dependent exposures: The G-formula and G-Estimation of Structural Nested Models. Later, he introduced a third class of models, [[Marginal structural model|Marginal Structural Models]], in which the parameters are estimated using inverse probability of treatment weights. He has also contributed significantly to the theory of dynamic treatment regimes, which are of high significance in [[comparative effectiveness research]] and personalized medicine. Together with Andrea Rotnitzky and other colleagues, in 1994 he introduced doubly robust estimators (derived from the influence functions) for statistical parameters in causal inference and missing data problems. The theory for doubly robust estimators has been highly influential in the field of [causal inference] and has influenced practice in computer science, biostatistics, epidemiology, machine learning, social sciences, and statistics.<ref name=":3">Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, Marie Davidian, Doubly Robust Estimation of Causal Effects, American Journal of Epidemiology, Volume 173, Issue 7, 1 April 2011, Pages 761–767, https://doi.org/10.1093/aje/kwq439</ref><ref name=":4">https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Retrieved 28 November 2021.</ref> In 2008, he also developed the theory of higher-order influence functions for statistical functional estimation with collaborators including Lingling Li, Eric Tchetgen Tchetgen, and [[Aad van der Vaart]]. | | In his original paper on causal inference, Robins described two new methods for controlling for confounding bias, which can be applied in the generalized setting of time-dependent exposures: The G-formula and G-Estimation of Structural Nested Models. Later, he introduced a third class of models, [[Marginal structural model|Marginal Structural Models]], in which the parameters are estimated using inverse probability of treatment weights. He has also contributed significantly to the theory of dynamic treatment regimes, which are of high significance in [[comparative effectiveness research]] and personalized medicine. Together with Andrea Rotnitzky and other colleagues, in 1994 he introduced doubly robust estimators (derived from the influence functions) for statistical parameters in causal inference and missing data problems. The theory for doubly robust estimators has been highly influential in the field of [causal inference] and has influenced practice in computer science, biostatistics, epidemiology, machine learning, social sciences, and statistics.<ref name=":3">Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M. Alan Brookhart, Marie Davidian, Doubly Robust Estimation of Causal Effects, American Journal of Epidemiology, Volume 173, Issue 7, 1 April 2011, Pages 761–767, https://doi.org/10.1093/aje/kwq439</ref><ref name=":4">https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Retrieved 28 November 2021.</ref> In 2008, he also developed the theory of higher-order influence functions for statistical functional estimation with collaborators including Lingling Li, Eric Tchetgen Tchetgen, and [[Aad van der Vaart]]. |