Knowledge Tree
Causal Inference
Facts (11)
Sources
Track: Poster Session 3 - aistats 2026 virtual.aistats.org 5 facts
claimEstimation of the Average Treatment Effect (ATE) is a core problem in causal inference with strong connections to Off-Policy Evaluation in Reinforcement Learning.
claimExisting causal inference methods in social networks often rely on strong assumptions regarding network-induced confounding, which frequently fail in high-dimensional networks and limit the applicability of those methods.
claimThe authors applied their proposed causal inference methodology to examine the impact of Self-Help Group participation on financial risk tolerance.
claimThe causal inference estimator proposed by the authors achieves semiparametric efficiency under mild regularity conditions, which enables consistent uncertainty quantification.
procedureThe proposed methodology for causal inference in social networks integrates graph machine learning techniques with the double machine learning framework to estimate direct and peer effects in a single observational social network.
A Hilbertian approach to biological problems | PLOS Complex ... journals.plos.org Nov 5, 2024 2 facts
claimMethods developed for causal inference from gene perturbation experiments have validated the feasibility of discerning causal structures within gene regulatory networks.
referenceMeinshausen N, Hauser A, Mooij JM, Peters J, Versteeg P, and Bühlmann P published 'Methods for causal inference from gene perturbation experiments and validation' in the Proceedings of the National Academy of Sciences of the United States of America in 2016.
Published Studies — Johns Hopkins Center for Psychedelic and ... hopkinspsychedelic.org 1 fact
referenceThe paper 'Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond' was published in bioRxiv in 2026 by Loewinger, G., Stensrud, M. J., Nayak, S. M., Yaden, D., and Levis, A. W.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
referenceJiao et al. authored a comprehensive survey on the intersection of causal inference and deep learning.
Knowledge graphs - Amazon Science amazon.science 1 fact
procedureThe Measurement & Experimentation role at Amazon involves applying causal inference methods to measure the incremental impact of marketing campaigns versus counterfactuals, navigating measurement challenges across platforms including Meta attribution, LiveRamp, clean rooms, and onsite tracking, analyzing experiment results to provide optimization recommendations, and establishing guardrails and success criteria for campaign evaluation.
LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org Oct 23, 2025 1 fact
claimFuture research in Large Language Models (LLMs) and Knowledge Graphs (KGs) is expected to focus on integrating structured KGs into LLM reasoning mechanisms to enhance logical consistency, causal inference, and interpretability.