Causal Effects of Stochastic Interventions
Last updated on
Mon, May 16, 2022
Nima Hejazi
Assistant Professor of Biostatistics
My research broadly concerns the intersection of causal inference and machine learning, including developing statistical methodology tailored for modern experiments and observational studies in the biomedical and health sciences.
Publications
Efficient estimation of modified treatment policy effects based on the generalized propensity score
Continuous treatments have posed a significant challenge for causal inference, both in the formulation and identification of …
Causal survival analysis under competing risks using longitudinal modified treatment policies
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters …
Nonparametric causal mediation analysis for stochastic interventional (in)direct effects
Causal mediation analysis has historically been limited in two important regards: (i) a focus has traditionally been placed on binary …
medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R
The
medoutcon R package provides facilities for efficient estimation of path-specific (in)direct effects that measure the impact of a …
txshift: Efficient estimation of the causal effects of stochastic interventions in R
The
txshift R package aims to provide researchers in (bio)statistics, epidemiology, health policy, econometrics, and related …
Nima Hejazi, David Benkeser
Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials
The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. …
Causal mediation analysis for stochastic interventions
Mediation analysis in causal inference has traditionally focused on binary treatment regimes and deterministic interventions, as well …
Iván Díaz, Nima Hejazi
Talks
Efficient Estimation of Modified Treatment Policy Effects Based on the Generalized Propensity Score
Continuous treatment variables have posed a significant challenge for causal inference, both in the formulation and identification of …
Wed, Nov 17, 2021 10:30 AM
New York, New York, United States (remote due to COVID-19)
Nonparametric Estimation of the Generalized Propensity Score Based on the Highly Adaptive Lasso
Continuous treatment variables have posed a significant challenge for causal inference, both in the formulation and identification of …
Wed, May 19, 2021 6:30 AM
Oslo, Norway (remote due to COVID-19)
Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials
Causal inference has traditionally focused on the effects of static interventions, under which the magnitude of the treatment is set to …
Wed, Jan 20, 2021 12:00 PM
Seattle, Washington, United States (remote due to COVID-19)
Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials
Causal inference has traditionally focused on the effects of static interventions, under which the magnitude of the treatment is set to …
Wed, Dec 16, 2020 11:45 AM
Boston, Massachusetts, United States (remote due to COVID-19)
Evaluating the Causal Impacts of Vaccine-induced Immune Responses in Two-phase Vaccine Efficacy Trials
Causal inference has traditionally focused on the effects of static interventions, under which the magnitude of the treatment is set to …
Wed, Nov 4, 2020 1:30 PM
Berkeley, California, United States (remote due to COVID-19)
Evaluating the Causal Impacts of Vaccine-induced Immune Responses in Two-phase Vaccine Efficacy Trials
Causal inference has traditionally focused on the effects of static interventions, under which the magnitude of the treatment is set to …
Thu, Oct 15, 2020 9:00 AM
Berkeley, California, United States (remote due to COVID-19)
Efficient Estimation of Stochastic Intervention Effects in Causal Mediation Analysis
Mediation analysis in causal inference has traditionally centered on static interventions and binary exposures, with classical theory …
Thu, Aug 6, 2020 10:00 AM
Philadelphia, Pennsylvania, United States (remote due to COVID-19)
Nonparametric Causal Mediation Analysis for Stochastic Interventions
Mediation analysis in causal inference has traditionally centered on static interventions and binary exposures, with classical theory …
Wed, Apr 29, 2020 1:30 PM
Berkeley, California, United States (remote due to COVID-19)
Robust Inference on the Causal Effects of Stochastic Interventions Under Two-Phase Sampling, with Applications in Vaccine Efficacy Trials
Much of the focus of statistical causal inference has been devoted to assessing the effects of static interventions, which specify a …
Thu, Mar 21, 2019 3:00 PM
Berkeley, California, United States
Towards the Realistic, Robust, and Efficient Assessment of Causal Effects with Stochastic Shift Interventions
My PhD qualifying examination presentation. The faculty committee consisted of Nicholas Jewell (chair), Mark van der Laan, Alan Hubbard …
Fri, Sep 14, 2018 10:00 AM
Berkeley, California, United States
Robust Nonparametric Inference for Stochastic Interventions Under Multi-Stage Sampling
Perhaps too often, work in statistical causal inference focuses on the effect of deterministic interventions, under which, for each …
Mon, Apr 2, 2018 4:00 PM
Berkeley, California, United States