55 citations · 167 across the 40 of their papers we have counts for
44 papers
Functional Estimation under Proxy-Based Full-Law Identification
Helen Guo, AmirEmad Ghassami, Ilya Shpitser +1
We state general conditions under which the full-data law is identified in the presence of latent variables, leveraging key observed variables ("proxies") associated with unobserve…
MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis
Shi Bo, Debarghya Mukherjee, AmirEmad Ghassami
Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them. Modern biomedical studies often inv…
Causal Discovery in Structural VAR Models Under Equal Noise Variance
SeyedSina Seyedi HasanAbadi, Fahimeh Arab, Erfan Nozari +1
Causal discovery from multivariate time series is challenging when causal effects may occur both across time and within the same sampling interval. This issue is especially importa…
Proximal Causal Inference for Hidden Outcomes
Helen Guo, AmirEmad Ghassami, Ilya Shpitser +1
Methods that rely on proxies, without imposing strong parametric structure, are increasingly used to deal with unobserved variables in causal inference. One influential line of thi…
Model-Robust Direct Effect Under Confounder-Mediator Ambiguity
AmirEmad Ghassami
Direct effect analyses usually require deciding whether a focal variable is a pre-exposure confounder or a post-exposure mediator. In observational studies, that distinction may be…
Causal Imitation Learning Under Measurement Error and Distribution Shift
Shi Bo, AmirEmad Ghassami
We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and d…
A Sensitivity Analysis Framework for Causal Inference Under Interference
Matvey Ortyashov, AmirEmad Ghassami
In many applications of causal inference, the treatment received by one unit may influence the outcome of another, a phenomenon referred to as interference. Although there are seve…
Debiased Ill-Posed Regression
AmirEmad Ghassami, James M. Robins, Andrea Rotnitzky
In various statistical settings, the goal is to estimate a function which is restricted by the statistical model only through a conditional moment restriction. Prominent examples i…
Semiparametric Triple Difference Estimators
Sina Akbari, Negar Kiyavash, AmirEmad Ghassami
The triple difference causal inference framework is an extension of the well-known difference-in-differences framework. It relaxes the parallel trends assumption of the difference-…
Debiased Causal Mediation Analysis in Ultra-High-Dimensional Settings in the Presence of Interaction Effects
Shi Bo, AmirEmad Ghassami, Debarghya Mukherjee
Mediation analysis is a crucial tool for uncovering the mechanisms through which a treatment affects an outcome, providing deeper causal insights and guiding effective intervention…
Causal Data Fusion for Panel Data without a Pre-Intervention Period
Zou Yang, Seung Hee Lee, Julia R. Köhler +1
Traditional panel-data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactual means.…
Causal Discovery in Linear Models with Unobserved Variables and Measurement Error
Yuqin Yang, Mohamed Nafea, Negar Kiyavash +2
The presence of unobserved common causes and measurement error poses two major obstacles to causal structure learning, since ignoring either source of complexity can induce spuriou…
A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Jaron J. R. Lee, AmirEmad Ghassami, Ilya Shpitser
Causal inference is made challenging by confounding, selection bias, and other complications. A common approach to addressing these difficulties is the inclusion of auxiliary data…
Bias-Targeted Nonparametric Balancing for Stable Causal Mediation Analysis
Chang Liu, AmirEmad Ghassami
Influence function (IF)-based estimators are widely used in mediation analysis due to their modeling flexibility, but standard implementations require direct estimation of the dist…
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
Zixiao Wang, AmirEmad Ghassami, Ilya Shpitser
We consider the task of identifying and estimating a parameter of interest in settings where data is missing not at random (MNAR). In general, such parameters are not identified wi…
Partial Identification of Causal Effects Using Proxy Variables
AmirEmad Ghassami, Yunshu Zhang, Ilya Shpitser +1
Proximal causal inference is a framework for evaluating the causal effects in the presence of unmeasured confounding. For point identification, it leverages a pair of proxy variabl…
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea +3
We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms perta…
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
Ehsan Mokhtarian, Saber Salehkaleybar, AmirEmad Ghassami +1
We study experiment design for unique identification of the causal graph of a simple SCM, where the graph may contain cycles. The presence of cycles in the structure introduces maj…
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
AmirEmad Ghassami, Chang Liu, Alan Yang +3
We study identifying and estimating the causal effect of a treatment variable on a long-term outcome using data from an observational and an experimental domain. The observational…
Causal Inference with Hidden Mediators
AmirEmad Ghassami, Alan Yang, Ilya Shpitser +1
Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are availa…
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
Yuqin Yang, Mohamed Nafea, AmirEmad Ghassami +1
Linear structural causal models (SCMs) -- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources -- a…
Partially Intervenable Causal Models
AmirEmad Ghassami, Ilya Shpitser
Graphical causal models led to the development of complete non-parametric identification theory in arbitrary structured systems, and general approaches to efficient inference. Neve…
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Sina Akbari, Ehsan Mokhtarian, AmirEmad Ghassami +1
We consider the problem of learning the causal MAG of a system from observational data in the presence of latent variables and selection bias. Constraint-based methods are one of t…
Information Theoretic Measures for Fairness-aware Feature Selection
Sajad Khodadadian, Mohamed Nafea, AmirEmad Ghassami +1
Machine learning algorithms are increasingly used for consequential decision making regarding individuals based on their relevant features. Features that are relevant for accurate…