Paper traces Granger tests toward causal structure in dynamical models
The review says Granger tests detect predictive links among signals, not causal links among generating mechanisms.
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Stories tagged #causal-inference
Causal Framework Evaluates Interventions for AI Agent Trajectories
First method estimates causal effects of randomized interventions in AI agent trajectories using small datasets.
CausalWAM Separates Action Causes in World Action Models
Sparsity constraint enables causal WAMs to identify action-relevant latent variables, improving closed-loop robotic control efficiency.
CausalWAM separates action causes from scene prediction in world models
CausalWAM adds sparse routing to pretrained WAMs proving action-relevant latents are identifiable up to permutation for improved robotic closed-loop control.
Offline RL method handles domain shift and unmeasured confounding
CAT-CCB and CAT-CMDP use causal transportability and pessimistic optimization, with finite-sample suboptimality bounds.
Offline RL frames next-best action as a constrained decision process
In a rideshare coupon study, the authors report 19% higher lifetime value than the behavior policy.
DeDiCa: A Scalable Deconfounding Discrete Causal Generative Model
DeDiCa estimates discrete causal effects with hidden confounding using exact, millisecond interventional inference.
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
ProximalFM amortizes Bayesian proximal causal inference so a transformer estimates CATE posteriors under hidden confounding.
Reasoning Models Are Accurate but Unsound on Identification
CERTID benchmark shows frontier reasoning models give unsound causal identification answers, with false-claim rates varying seventeen-fold across Gemini Flash, Gemini Pro, and GPT-5.5.
Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness
The paper proves matching minimax regret bounds for causal logistic bandits under counterfactual fairness.
Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations
ARGUS, a language-model pipeline, audits identification assumptions in climate-policy difference-in-differences studies and abstains when evidence is missing.
LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence
LimiX-2 scales Contextual Mechanism Networks pretrained via context-conditional masked modeling, beating tabular foundation models on TabArena, TALENT, and BCCO benchmarks.
LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence
LimiX-2, a tabular foundation model built on Contextual Mechanism Networks, outperforms existing tabular models on TabArena, TALENT, and BCCO.
Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.
Causal Foundation Models
A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.