Hugging Face Trending Papers
Sep 21

Exponential Family Synthetic Controls

We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the panel corresponds to a dataset drawn from an exponenti...

arXiv Machine Learning
Sep 22

Causal Inference with Unobserved Confounding: A Mixture Learning Perspective

The paper introduces a mixture‑learning framework for causal inference with unobserved confounding, treating latent confounders as sources of heterogeneity that create mixture structures in observed data. By assuming suitable structural and identifiability conditions, it shows that recovering the mixing distribution and component mechanisms allows estimation of interventional distributions and causal estimands. The authors illustrate the approach with Bernoulli mixture examples, extend it to high‑dimensional exponential‑family mixtures with dependent outcomes, and relate it to panel‑data settings, latent factor models, and synthetic interventions.

By Mansi Sood, Devavrat Shah
arXiv Statistics ML
3d ago

Identifying Causal Effects Using a Single Proxy Variable

The paper tackles the problem of estimating causal effects when an unobserved confounder is present. It assumes a single, possibly multi‑dimensional proxy variable for the confounder and knowledge of the mechanism that generates this proxy. Under the Single Proxy Identifiability of Causal Effects (SPICE) assumption, the authors prove that the error mechanism is complete and causal effects are identifiable, extending prior proxy‑based results to continuous, multi‑dimensional settings and more flexible functional forms. They also introduce SPICE‑Net, a neural‑network‑based framework for estimating causal effects applicable to both discrete and continuous treatments.

By Silvan Vollmer, Niklas Pfister, Sebastian Weichwald
arXiv Machine Learning
Sep 23

Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.

By Saksham Jain, Alex Luedtke