1 citations · 2 across the 17 of their papers we have counts for
17 papers
Relaxing Faithfulness with Intervention-Only Causal Discovery
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence prope…
The Spectral Structure of Latent Treatment Effects
Hamza Virk, Bijan Mazaheri, Yihren Wu
Identifying heterogeneous treatment effects under unobserved confounding is central in observational causal inference. In proxy models with a discrete latent confounder, prior Synt…
Causal Foundations of Collective Agency
Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis Hammond
A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distin…
Data Augmentation via Causal-Residual Bootstrapping
Mateusz Gajewski, Sophia Xiao, Bijan Mazaheri
Data augmentation integrates domain knowledge into a dataset by making domain-informed modifications to existing data points. For example, image data can be augmented by duplicatin…
Masking Causality and Conditional Dependence
Zou Yang, Sophia Xiao, Bijan Mazaheri
Many regulatory and analytic problems require that a prohibited variable influence a decision only through a designated allowable channel -- a conditional-independence requirement…
Estimating Aleatoric Uncertainty in the Causal Treatment Effect
Liyuan Xu, Bijan Mazaheri
Previous work on causal inference has primarily focused on averages and conditional averages of treatment effects, with significantly less attention on variability and uncertainty…
Meta-Dependence in Conditional Independence Testing
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
Conditional independence testing is a critical component of feature screening, invariant statistical models, and causal discovery. Many of these algorithms rely on the sequential a…
Synthetic Potential Outcomes and Causal Mixture Identifiability
Bijan Mazaheri, Chandler Squires, Caroline Uhler
Heterogeneous data from multiple populations, sub-groups, or sources is often represented as a ``mixture model'' with a single latent class influencing all of the observed covariat…
Causal Discovery in Mixtures of Populations
Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1
Causal discovery aims to learn causal structures up to certain symmetries. Diverse populations or changing environments give rise to heterogeneous data in the following sense: each…
Omitted Labels Induce Nontransitive Paradoxes in Causality
Bijan Mazaheri, Siddharth Jain, Matthew Cook +1
We explore "omitted label contexts," in which training data is limited to a subset of the possible labels. This setting is standard among specialized human experts or specific, foc…
Identification of Mixtures of Discrete Product Distributions in Near-Optimal Sample and Time Complexity
Spencer L. Gordon, Erik Jahn, Bijan Mazaheri +2
We consider the problem of identifying, from statistics, a distribution of discrete random variables that is a mixture of product distributions. The best previ…
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing +1
Statistical prediction models are often trained on data from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts…
Causal Inference Despite Limited Global Confounding via Mixture Models
Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani +1
A Bayesian Network is a directed acyclic graph (DAG) on a set of random variables (the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the ran…
Expert Graphs: Synthesizing New Expertise via Collaboration
Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck
Consider multiple experts with overlapping expertise working on a classification problem under uncertain input. What constitutes a consistent set of opinions? How can we predict th…
Source Identification for Mixtures of Product Distributions
Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani +1
We give an algorithm for source identification of a mixture of product distributions on bits. This is a fundamental problem in machine learning with many applications. Our…
Robust Correction of Sampling Bias Using Cumulative Distribution Functions
Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck
Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift. Current approaches for alleviating this oft…
The Sparse Hausdorff Moment Problem, with Application to Topic Models
Spencer Gordon, Bijan Mazaheri, Leonard J. Schulman +1
We consider the problem of identifying, from its first noisy moments, a probability distribution on of support . This is equivalent to the problem of learning…