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This paper introduces lifted causal inference, leveraging parametric causal factor graphs to efficiently compute causal effects in relational domains, and presents the Lifted Causal Inference (LCI) algorithm for polynomial-time inference.
TabPFN-CFM is a causal foundation model that predicts both causal structure and outcomes from observational data, supporting all three levels of Pearl's Causal Hierarchy and achieving improved performance over baselines.
This paper conducts a same-hands re-evaluation of bivariate causal direction methods on the Tübingen cause-effect pairs, introducing a parameter-free compression baseline that ties with SLOPE. It documents how published accuracy figures are inflated by protocol differences and releases all code and data.
This survey provides a systematic review of federated causal discovery and inference, organizing methods by methodological paradigm, federation topology, and structural scope, and highlighting open challenges.
This paper introduces causal reinforcement learning (CRL), unifying causal inference and reinforcement learning under a structural causal model framework, and explores novel learning settings such as generalized policy learning and counterfactual learning.
A Microsoft study using 43 weeks of data from 16,223 engineers found that GitHub Copilot increases pull request completion by 40.5% when holding development effort constant.
A new paper using instrumental variables finds that data centers actually caused average retail electricity rates to fall modestly in the US from 2015-2024, contrary to popular belief.
The Stanford REAP team has launched CoPaper.AI, a tool that can automatically generate a reproducible empirical paper with complete Stata/R code and charts within 30 minutes after inputting raw data, aiming to end the manual labor of traditional papers.
Artemis proposes a region-level causal framework that learns region-specific confounder representations to eliminate demographic confounders in multimodal neuroimaging, improving graph neural network performance on disease diagnosis and classification tasks.
This paper systematically evaluates the impact of classification model selection within the InferBERT framework for causal adverse drug event detection, finding that domain-specific pre-training (BioBERT) outperforms both simpler models and larger LLMs like Med-LLaMA.
Proposes DAG-SHAP, a novel feature attribution method based on edge intervention for directed acyclic graphs, addressing limitations of existing Shapley value methods in capturing feature interactions and causal relationships.
This paper introduces relational structural causal models, extending structural causal models to settings with varying objects and relations. It provides theoretical results for identification and proposes relational neural causal models that outperform non-relational baselines on simulated traffic scenes.
This paper develops a Bayesian deep learning framework to estimate the causal effect of air pollution regulations on PM2.5 concentrations in London from 2010 to 2020, finding an average reduction of 1.88 μg/m³ (12.35%).
This paper proposes Dose-AIPTB, a framework for estimating the individual probability of treatment benefit under discrete dose assignments using attention-based aggregation, outperforming kernel alternatives in numerical experiments.
This paper investigates whether topic sentiment causally affects perceived political ideology in news articles, comparing human annotations from AllSides with those from LLMs including GPT-4o-mini and Llama-3.3-70B. It finds that fine-tuned GPT-4o-mini exhibits a spurious sentiment-ideology coupling not present in human judgments, highlighting risks of using LLM annotations as proxies in causal analyses.
This paper studies off-policy evaluation (OPE) when decision subjects (agents) strategically modify their covariates in response to a policy. It proposes a method that uses local disclosure via post-hoc explanations to reveal agents' pre-strategic covariates and construct a doubly robust estimator for policy value.
StableRCA is a novel root cause analysis framework that identifies intervention targets by estimating local Markov boundaries and detecting conditional distribution shifts, avoiding the need for global causal graph discovery and demonstrating robustness across synthetic and real-world datasets.
Introduces CausalPOI, a spatio-temporal graph-based causal representation learning framework for cold-start POI check-in forecasting, which outperforms state-of-the-art baselines on real-world SafeGraph datasets.
Proposes CVT-RL, a constrained policy-gradient algorithm with policy-conditioned counterfactual contribution estimation and verifiable rewards, improving long-horizon language agent reliability and reducing reward hacking.
This paper introduces a text-based causal inference methodology using an enhanced CausalBERT to disentangle the effects of individual aspects (e.g., school administration, academic performance) on overall online review ratings, validated on 600K+ U.S. K-12 school reviews. Key improvements include temperature scaling, hyperparameter optimization, and interpretability methods to reduce confounding bias.