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DTSTAMP:20251010T205248Z
DESCRIPTION:Nima Hejazi\, PhD\n\nAssistant Professor\, Department of Biosta
 tistics\n	Harvard Chan School of Public Health\n\nWHEN: Wednesday\, October
  22\, 2025\, from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 9IÖÆ×÷³§Ãâ·Ñ College A
 venue\, Rm 1140\; Zoom\n	NOTE: Nima Hejazi will be presenting virtually fro
 m Boston\n	 \n\nAbstract\n\nIn contemporary studies in environmental health
  and epidemiology\, observational data feature structure that poses severa
 l distinct challenges for causal inference. Distinct problems include the 
 presence of continuous exposures\; many measured putative confounders of t
 he causal relationship of the exposure and outcome\; and network interfere
 nce\, wherein the exposure of one unit may affect the outcome of its neigh
 bors. Each problem requires care to address. We introduce a novel interven
 tion scheme—the induced modified treatment policy—designed to aid in the i
 dentification of the causal effect of intervening on a continuous exposure
  while simultaneously addressing network interference between study units\
 ; this addresses the first and third of the challenges noted above. Buildi
 ng on a fast-growing literature in causal machine learning\, we formulate 
 asymptotically (semi-parametric) efficient estimators of the causal effect
 s of induced modified treatment policies\, which allow for the use of non-
 parametric regression and/or machine learning techniques for estimation of
  nuisance functions\; this addresses the second challenge noted above by a
 llowing for the data analyst to flexibly adjust for many putative confound
 ers while acknowledging the limitations of domain knowledge in practice. I
 n numerical experiments\, we illustrate how induced modified treatment pol
 icies eliminate causal (i.e.\, identification) bias due to network interfe
 rence. We apply the proposed methodology to evaluate the causal effect of 
 zero-emission vehicle uptake on air pollution in California\, strengthenin
 g evidence from prior analytic studies.\n\n\n	Speaker Bio\n\nI am an assist
 ant professor of biostatistics at the Harvard Chan School of Public Health
 . My research interests primarily center on causal inference\, semi-parame
 tric statistics (particularly causal or de-biased machine learning)\, assu
 mption-lean and non-parametric inference\, and computational statistics. M
 y methodological work is usually motivated by applied science investigatio
 ns in the infectious disease sciences\, the study of chronic diseases\, an
 d cancer science. I’m also deeply interested in both open-source software 
 and high-performance computing for the statistical sciences—to push the bo
 undaries of statistical methodology and to promote transparency and reprod
 ucibility in the practice of applied statistics and statistical data scien
 ce. \n	\n	Website: nimahejazi.org\n
DTSTART:20251022T193000Z
DTEND:20251022T203000Z
SUMMARY:Evaluating the Causal Effects of Continuous Exposures under Network
  Interference with Induced Modified Treatment Policies
URL:/spgh/channels/event/evaluating-causal-effects-con
 tinuous-exposures-under-network-interference-induced-modified-treatment-36
 8246
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