James Robins

Robins attended Harvard College with the class of 1971, concentrating in mathematics and philosophy. He was elected to Phi Beta Kappa, but did not graduate. He went on to attend Washington University School of Medicine, graduating in 1976,Thomas S. Richardson and Andrea Rotnitzky, [https://projecteuclid.org/journals/statistical-science/volume-29/issue-4/Causal-Etiology-of-the-Research-of-James-M-Robins/10.1214/14-STS505.full Causal Etiology of the Research of James M. Robins], Statist. Sci. 29 (4) 459-484, 2014. [https://doi.org/10.1214/14-STS505 doi:10.1214/14-STS505] and practiced Occupational Medicine for several years. While working in occupational medicine, he attended basic courses in applied medical statistics at the Yale School of Public Health, but quickly came to the conclusion that the methodology used at the time was insufficiently rigorous to support causal conclusions.

In 1986, Robins introduced a new framework for drawing causal inference from observational data.{{Cite journal |last=Robins |first=James |date=1986 |title=A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect |url=https://linkinghub.elsevier.com/retrieve/pii/0270025586900886 |journal=Mathematical Modelling |language=en |volume=7 |issue=9–12 |pages=1393–1512 |doi=10.1016/0270-0255(86)90088-6|url-access=subscription }} In this and 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.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

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 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.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 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.

• {{cite journal

| author = Robins, J.M.

| year = 1989

| title = The control of confounding by intermediate variables

| journal = Statistics in Medicine

| volume = 8

| pages = 679–701

| doi = 10.1002/sim.4780080608

| pmid = 2749074

| issue = 6

}}

• {{cite journal | author = Robins, J.M. |author2=Tsiatis, A.A. | year = 1991 | title = Correcting for non-compliance in randomized trials using rank preserving structural failure time models | journal = Communications in Statistics - Theory and Methods | volume = 20 | issue = 8 | pages = 2609–2631 | doi = 10.1080/03610929108830654 }}
• {{cite journal | author = Robins, J.M. | year = 1994 | title = Correcting for non-compliance in randomized trials using structural nested mean models | journal = Communications in Statistics - Theory and Methods | volume = 23 | issue = 8 | pages = 2379–2412 | doi = 10.1080/03610929408831393 }}
• {{cite book

| author = Robins, J.M.

| year = 1997

| chapter = Causal inference from complex longitudinal data

| title = Latent Variable Modeling and Applications to Causality

| volume = 120

| pages = 69–117

| series = Lecture Notes in Statistics

| editor= M. Berkane

| publisher= Springer-Verlag

}}

• {{cite journal

| author = Robins, J.M.

|author2=Ritov, Y.

| year = 1997

| title = Toward A Curse Of Dimensionality Appropriate (CODA) Asymptotic Theory For Semi-parametric Models

| journal = Statistics in Medicine

| volume = 16

| issue = 3

| pages = 285–319

| doi = 10.1002/(SICI)1097-0258(19970215)16:3<285::AID-SIM535>3.3.CO;2-R

| pmid=9004398

}}

• {{cite journal

| author = Robins, J.M.

| year = 1998

| title = Correction for non-compliance in equivalence trials

| journal = Statistics in Medicine

| volume = 17

| issue = 3

| pages = 269–302

| doi = 10.1002/(SICI)1097-0258(19980215)17:3<269::AID-SIM763>3.0.CO;2-J

| pmid = 9493255

}}

• {{cite journal

| author = Robins, J.M. |author2=Hernan, M.A. |author3=Brumback, B.|author3-link= Babette Brumback

| year = 2000

| title = Marginal Structural Models and Causal Inference in Epidemiology

| journal = Epidemiology

| volume = 11

| issue = 5

| pages = 550–560

| doi = 10.1097/00001648-200009000-00011

| pmid=10955408

| jstor = 3703997

|citeseerx=10.1.1.116.7039 |s2cid=8907527 }}

• {{cite book

| author = van der Laan, M.J.

|author2=Robins, J.M.

| year = 2003

| title = Unified Methods for Censored Longitudinal Data and Causality

| publisher = Springer

| series = Springer Series in Statistics

| isbn = 978-0-387-95556-8

}}

{{Reflist}}

• [http://www.hsph.harvard.edu/faculty/james-robins/ James Robins — Mitchell L. and Robin LaFoley Dong Professor of Epidemiology]. Harvard School of Public Health (Accessed 15 March 2008).
• [http://www.hsph.harvard.edu/~robins/ Dr. James M. Robins — Bibliography] Harvard School of Public Health (Accessed 15 March 2008).
• Gehrman, Elizabeth (March 23, 2006) [https://web.archive.org/web/20080317055338/http://www.news.harvard.edu/gazette/2006/03.23/13-iqss.html James Robins makes statistics tell the truth: Numbers in the service of health]. Harvard University Gazette.

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{{DEFAULTSORT:Robins, James}}

Category:American epidemiologists

Category:Year of birth missing (living people)

Category:Harvard T.H. Chan School of Public Health faculty

Category:Living people

Category:Washington University School of Medicine alumni

Category:American biostatisticians

Category:Fellows of the American Statistical Association

Category:Harvard College alumni