7 papers
Debiased Counterfactual Generation via Flow Matching from Observations
Hugh Dance, Johnny Xi, Peter Orbanz +1
Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing approaches model the counterfactua…
Randomization Tests for Conditional Group Symmetry
Kenny Chiu, Alex Sharp, Benjamin Bloem-Reddy
Symmetry plays a central role in the sciences, machine learning, and statistics. While statistical tests for the presence of distributional invariance with respect to groups have a…
Counterfactual Cocycles: A Framework for Robust and Coherent Counterfactual Transports
Hugh Dance, Benjamin Bloem-Reddy
Estimating joint distributions (a.k.a. couplings) over counterfactual outcomes is central to personalized decision-making and treatment risk assessment. Two emergent frameworks wit…
CN-SBM: Categorical Block Modelling For Primary and Residual Copy Number Variation
Kevin Lam, William Daniels, J Maxwell Douglas +4
Cancer is a genetic disorder whose clonal evolution can be monitored by tracking noisy genome-wide copy number variants. We introduce the Copy Number Stochastic Block Model (CN-SBM…
Distinguishing Cause from Effect with Causal Velocity Models
Johnny Xi, Hugh Dance, Peter Orbanz +1
Bivariate structural causal models (SCM) are often used to infer causal direction by examining their goodness-of-fit under restricted model classes. In this paper, we describe a pa…
Identifying Metric Structures of Deep Latent Variable Models
Stas Syrota, Yevgen Zainchkovskyy, Johnny Xi +2
Deep latent variable models learn condensed representations of data that, hopefully, reflect the inner workings of the studied phenomena. Unfortunately, these latent representation…