# Causal Effects based on Stochastic Interventions

Last updated on
Fri, Oct 23, 2020

##### Nima Hejazi

###### NSF Postdoctoral Research Fellow in Biostatistics

My research interests lie at the intersection of causal inference and machine learning, especially as applied to the statistical analysis of complex data from observational studies and experiments in the biomedical and health sciences.

## Publications

### 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

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