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: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.
The paper introduces a path‑specific version of the fraction of negatively affected (FNA) to separate total harm into direct and indirect components in causal mediation settings. Because these components depend on joint distributions of potential outcomes that are not point‑identified, the authors develop a partial identification framework, deriving sharp Makarov bounds and a semiparametric efficient estimator with valid confidence intervals under mild margin conditions. The framework is illustrated through numerical experiments, marking the first study of path‑specific harm decomposition and its orthogonal inference.
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:2602. 22083v2 Announce Type: replace-cross Abstract: Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of total and mediated causal effects in DAGs with hidden variables.
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. 21185v2 Announce Type: replace-cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed.
arXiv:2609.22383v1 Announce Type: cross Abstract: Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument gen...
arXiv:2503. 20546v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables.
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:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.
arXiv:2609.00071v1 Announce Type: new Abstract: Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance ma...
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.