43 citations · 143 across the 40 of their papers we have counts for
25 papers · 1 filter
Formulating Cross-World Mediation Estimands Through Single-World Mixtures
Razieh Nabi, David Benkeser
In causal mediation analysis, natural direct and indirect effects are defined through nested counterfactuals that combine the outcome under one exposure level with the mediator val…
Toward a Semiparametric Efficiency Theory under Equality Constraints in Nested Markov Models
Razieh Nabi, Anna Guo, Lin Liu
Probabilistic models of Directed Acyclic Graphs (DAGs) with latent variables impose equality constraints on the observed data distribution beyond ordinary conditional independencie…
Semiparametric Efficiency Theory as Differential Calculus on a Space of Probability Distributions
Razieh Nabi
Semiparametric efficiency theory provides the mathematical foundation for influence-function-based estimation, including one-step estimators, targeted minimum loss estimators, and…
Inferring Comprehensive Cohort Causal Effects in the Presence of Unmeasured Confounding and Missing Outcomes
Shiyao Xu, Razieh Nabi, Martin Underwood +1
This paper presents a methodological framework for estimating the comprehensive cohort causal effect (CCCE) in mixed-design clinical studies that combine randomized controlled tria…
Causal Sufficient Dimension Reduction for Multiple Continuous Exposures with an Application to Environmental Mixtures
Thomas W. Hsiao, Howard H. Chang, Razieh Nabi
Estimating causal effects with multivariate continuous exposures is challenging because causal exposure-response surfaces can be high-dimensional, complicating estimation and inter…
Causal Vaccine Effects on Post-infection Outcomes in the Naturally Infected
Allison Codi, Elizabeth Rogawski McQuade, Razieh Nabi +3
Understanding vaccine effects on post-infection outcomes is critical for evaluating the full value proposition of a vaccine. However, defining appropriate causal effects on such ou…
Controllable Generative Sandbox for Causal Inference
Qi Zhang, Harsh Parikh, Ashley Naimi +3
Method validation and study design in causal inference rely on synthetic data with known counterfactuals. Existing simulators trade off distributional realism, the ability to captu…
Weighting-Based Identification and Estimation in Graphical Models of Missing Data
Anna Guo, Razieh Nabi
We propose a constructive algorithm for identifying complete data distributions in graphical models of missing data. The complete data distribution is unrestricted, while the missi…
Causal Inference for Preprocessed Outcomes with an Application to Functional Connectivity
Zihang Wang, Razieh Nabi, Benjamin B. Risk
In biomedical research, repeated measurements within each subject are often processed to remove artifacts and unwanted sources of variation. The resulting data are used to construc…
Coarsening Bias from Variable Discretization in Causal Functionals
Xiaxian Ou, Razieh Nabi
Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of tota…
Causal Inference with the Napkin Graph
Anna Guo, Lin Liu, David Benkeser +1
Unmeasured confounding can render identification strategies based on adjustment functionals invalid. We study the "Napkin" graph, a causal structure that encapsulates features of M…
Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser +1
We are grateful to the discussants, Levis and Kennedy [2025], Luo and Geng [2025], Wang and van der Laan [2025], and Yang and Kim [2025], for their thoughtful comments on our paper…
Target trial emulation without matching: a more efficient approach for evaluating vaccine effectiveness using observational data
Emily Wu, Elizabeth Rogawski McQuade, Mats Stensrud +2
Real-world vaccine effectiveness has increasingly been studied using matching-based approaches, particularly in observational cohort studies following the target trial emulation fr…
Self-separated and self-connected models for mediator and outcome missingness in mediation analysis
Trang Quynh Nguyen, Razieh Nabi, Fan Yang +2
Missing data is a common challenge in studying treatment effects. In the context of mediation analysis, this paper addresses missingness in the mediator and outcome, focusing on id…
Average Causal Effect Estimation in DAGs with Hidden Variables: Beyond Back-Door and Front-Door Criteria
Anna Guo, Razieh Nabi
The identification theory for causal effects in directed acyclic graphs (DAGs) with hidden variables is well established, but methods for estimating and inferring functionals that…
Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
Anna Guo, David Benkeser, Razieh Nabi
Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed c…
Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms
Anna Guo, Jiwei Zhao, Razieh Nabi
Conducting valid statistical analyses is challenging in the presence of missing-not-at-random (MNAR) data, where the missingness mechanism is dependent on the missing values themse…
Graphical Models of Entangled Missingness
Ranjani Srinivasan, Rohit Bhattacharya, Razieh Nabi +2
Despite the growing interest in causal and statistical inference for settings with data dependence, few methods currently exist to account for missing data in dependent data settin…
Ananke: A Python Package For Causal Inference Using Graphical Models
Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi +1
We implement Ananke: an object-oriented Python package for causal inference with graphical models. At the top of our inheritance structure is an easily extensible Graph class that…
Causal and Counterfactual Views of Missing Data Models
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser +1
It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficu…
On Testability of the Front-Door Model via Verma Constraints
Rohit Bhattacharya, Razieh Nabi
The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome. However, the key assump…
On Testability and Goodness of Fit Tests in Missing Data Models
Razieh Nabi, Rohit Bhattacharya
Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acycl…
Semiparametric sensitivity analysis: unmeasured confounding in observational studies
Razieh Nabi, Matteo Bonvini, Edward H. Kennedy +3
Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn fro…
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbia…