On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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← all fields·9 papers on “causal inference” in Statistical ML · 1 month

Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inferenceecon.EM

New methods use vector search and nearest-neighbor matching for policy learning in causal inference.

problem Learning optimal policies in causal inference with limited data.

method RAG-based policy learning with vector search and nearest-neighbor matching.

result The methods bound the within-candidate choice regret and evaluate the one-step method directly as a policy.

policy learning causal inference vector search nearest-neighbor matching retrieval-augmented generation

2026-07-20abs ↗pdf ↗

Verifying formulas for interventional distributionsstat.ME

New method verifies formulas for causal interventional distributions.

problem Deciding if a given formula correctly identifies an interventional distribution.

method Proposed a falsifier to check if a formula is identifying.

result Falsifier can induce an almost-surely correct verifier for certain models.

causal inference interventional distributions verification falsifier exponential-family models

2026-07-15abs ↗pdf ↗

Contrast-Free ICA and Causal Inference via Wasserstein Distances to the Gaussianstat.ML

Study uses Wasserstein distance to identify causal orders and unmix sources.

problem Identifying causal relationships and separating sources in non-Gaussian data.

method Wasserstein distance for non-Gaussianity, linear ICA, causal inference.

result Exact identification of ICA unmixing matrix and causal orders.

Wasserstein distance ICA causal inference non-Gaussian data LiNGAM

2026-07-14abs ↗pdf ↗

Falsifying Causal Graphs With Outlier Eventsstat.ML

New method falsifies causal graphs using outlier events.

problem Inferring causal relationships from data is hard.

method Falsify candidate causal graphs based on outlier propagation.

result Statistical tests control false positives and have power guarantees.

causal inference outlier analysis graph theory data science

2026-07-13abs ↗pdf ↗

Geometric Causal Modelsstat.ML

Develops geometric causal models for causal inference from dependent data.

problem Causal inference from structured, dependent data (e.g., spatial, network, molecular).

method Geometric causal models (GCMs) exploiting symmetries of data generating process, combining group theory, ergodic theory, and Bayesian inference.

result Establishes identification and estimation of causal effects from dependent data.

causal inference dependent data symmetry group theory ergodic theory

2026-07-06abs ↗pdf ↗

Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Informationstat.ME

This paper identifies and bounds ICE central moments using PO marginal central moments.

problem Identifying and characterizing treatment effect heterogeneity.

method Using only marginal central moments of potential outcomes, the paper identifies and bounds central moments of individual causal effects.

result Identification and bounding of central moments of ICE using marginal moments of POs.

causal inference central moments individual causal effects potential outcomes marginal moments

2026-07-06abs ↗pdf ↗

Outcome-adapted Automatic Debiased Machine Learningstat.ME

Improved AutoDML estimator for causal inference using outcome-adapted shared covariate representation.

problem Efficiency in estimating treatment or policy effects in causal inference.

method Outcome-adapted AutoDML estimator that uses a shared covariate representation that is predictive of the outcome but not the Riesz representer.

result Outcome-adapted AutoDML estimator is asymptotically more efficient than baseline AutoDML.

causal inference outcome-adapted AutoDML shared representation Riesz representer

2026-07-03abs ↗pdf ↗

Non-parametric recovery of causal diffusion mechanisms from steady-state observationsstat.ML

Method recovers causal diffusion mechanisms from steady-state data without parametric assumptions.

problem Recovering causal diffusion mechanisms from steady-state gene expression data.

method Non-parametric kernel estimator for drift function, cross-validation for hyperparameter tuning.

result Full causal mechanism can be non-parametrically identified under weak non-explosion criterion.

causal inference diffusion processes stochastic systems steady-state data non-parametric estimation

2026-06-29abs ↗pdf ↗

Learning heterogeneous treatment effects under principal stratificationstat.ME

Study identifies and estimates treatment effect heterogeneity within principal stratification subpopulations.

problem Causal inference with intermediate outcomes and treatment effect heterogeneity.

method Proposes a novel doubly cross-fit doubly robust machine learner to efficiently learn conditional principal causal effects under principal ignorability.

result Demonstrates informative patterns of treatment effect heterogeneity within the always-survivor subpopulation in an acute lung injury trial.

causal inference treatment effect principal stratification heterogeneity machine learning

2026-06-27abs ↗pdf ↗