← Back to all news
arXiv Statistics ML By Yingjie Feng

Causal Inference in Possibly Nonlinear Factor Models

Read the original on arXiv Statistics ML →

The paper introduces a causal inference method for treatment effect models where confounders are measured noisily. It leverages many noisy proxies linked to latent confounders through an unknown, possibly nonlinear factor structure, using a local principal subspace approximation that combines K‑nearest‑neighbor matching and principal component analysis. The authors construct doubly‑robust estimators for various causal parameters, establish their large‑sample properties, and provide uniformly consistent estimators of the conditional average treatment effect, illustrated with an empirical study on political connections and stock returns and a Monte Carlo experiment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

Related stories

arXiv Statistics ML
4d 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
More like this →
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
More like this →
arXiv AI
Aug 24

Foundation Models for Partial Causal Identification

The paper introduces causal foundation models that can bound the effects of interventions and counterfactuals using only observational data. It defines a canonical prior with full support over structural causal models with discrete observables, enabling the translation of counterfactual bounding into learning distributions over functions that map data and structural assumptions to causal queries. This approach extends causal foundational modelling to partially-identifiable causal effects, where unobserved confounding leads to multiple compatible values for the effect.

By Alexis Bellot, Anish Dhir
More like this →