1 citations · 1 across the 7 of their papers we have counts for
16 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…