🔗 Causal Inference

🤖 AAAI2026 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (4) · 🔬 ICLR2026 (64) · 💬 ACL2026 (7) · 🧪 ICML2026 (19) · 🧠 NeurIPS2025 (19) · 📹 ICCV2025 (2)

Causal Inference Under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects

This paper proposes the BMTM/HBMTM Bayesian mixture model framework. In scenarios where consumers strategically manipulate spending to reach reward thresholds, the framework decomposes the observed distribution into bunching and non-bunching sub-distributions to accurately estimate threshold causal effects and heterogeneous treatment effects across subgroups.

CaDyT: Causal Structure Learning for Dynamical Systems with Theoretical Score Analysis

This paper proposes CaDyT, which combines Gaussian process-based continuous-time dynamics modeling (via Adams-Bashforth integrators for exact inference) with the Minimum Description Length (MDL) principle for structure search. The method simultaneously addresses irregular sampling and causal structure identification, substantially outperforming all baselines on double-mass spring, diamond graph, and Rössler oscillator benchmarks (AUPRC 0.79 vs. runner-up 0.39).

From Theory of Mind to Theory of Environment: Counterfactual Simulation of Latent Environmental Dynamics

This paper proposes the concept of "Theory of Environment" (ToE), arguing that humans may infer latent environmental dynamics through computational mechanisms shared with Theory of Mind (ToM), thereby expanding the dimensionality of motor exploration and facilitating behavioral innovation.

I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables

This paper proposes I-CAM-UV, a method that enumerates consistent DAGs satisfying structural constraints derived from multiple CAM-UV outputs over non-identical variable sets, recovering causal relations lost due to unobserved variables, and introduces an optimal-first search algorithm exploiting cost monotonicity for efficient combinatorial search.

KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education

This paper proposes KTCF, a counterfactual explanation generation method for Knowledge Tracing (KT) that leverages inter-concept relationships to produce sparse and actionable counterfactuals, subsequently post-processed into sequentially ordered instructional recommendations. KTCF comprehensively outperforms baseline methods across validity, sparsity, and actionability metrics.

Learning Subgroups with Maximum Treatment Effects without Causal Heuristics

Under the SCM framework, the paper proves that the subgroup with maximum treatment effect must exhibit homogeneous pointwise effects (Theorem 1); under the partition model assumption, it proves that optimal subgroup discovery reduces to standard supervised learning (Theorem 2), achievable via CART with the Gini index. On 77 ACIC-2016 semi-synthetic datasets, the proposed method achieves a mean treatment effect of 10.54 (vs. 7.84 for the runner-up), ranking first on 51.9% of datasets.

Sparse Additive Model Pruning for Order-Based Causal Structure Learning

This paper proposes SARTRE, a framework that employs randomized tree embeddings and group-sparse regression to learn sparse additive models, replacing the hypothesis-testing-based redundant edge pruning in CAM-pruning for order-based causal structure learning. SARTRE achieves significant speedups without sacrificing accuracy.