arXiv:2605. 01765v2 Announce Type: replace-cross Abstract: Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional changes induced by complex and nonlinear causal mechanisms.
arXiv:2607. 10540v1 Announce Type: cross Abstract: We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation under the "no essential heterogeneity" (NEH) assumption.
arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.
CIDER-FM is a causal foundation model that combines finite observational data with surrogate-interventional datasets to predict target conditional interventional distributions more accurately than using observational data alone. It employs an intervention-aware representation and hierarchical three‑axis attention to integrate information across variables, samples, and experimental regimes. Experiments on synthetic graphs, simulated data, and real‑world Causal Chambers data show that incorporating experimental context improves CID prediction performance.
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.
arXiv:2606. 30648v1 Announce Type: cross Abstract: Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them.
arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.
arXiv:2105. 09254v4 Announce Type: replace-cross Abstract: In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interest.
arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.
Causal Foundation Models (CFMs) are pretrained neural networks designed to estimate causal quantities—such as the average treatment effect—across new datasets using in‑context learning, eliminating the need for bespoke pipelines or model updates. The paper introduces CFMs, reviews foundational concepts in causal inference and machine learning, and provides practical code examples and Jupyter notebooks to illustrate their application.
arXiv:2503.17894v4 Announce Type: replace-cross Abstract: We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effect...
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.
arXiv:2606. 06288v1 Announce Type: cross Abstract: Causal representation learning aims to infer the high-level latent causal concepts that give rise to observed low-level measurements.