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Causal Inference

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Papers

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Showing 451–500 of 1722 papers

TitleStatusHype
A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series—0
Out-of-distribution robustness for multivariate analysis via causal regularisation—0
Applied Causal Inference Powered by ML and AICode2
The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing—0
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal InferenceCode0
History-dependence shapes causal inference of brain-behaviour relationshipsCode0
CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity LearningCode1
How to Understand "Support"? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding—0
Identifying Assumptions and Research Dynamics—0
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect EstimatorsCode0
Language Models Represent Beliefs of Self and OthersCode1
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inferenceCode0
Towards Generalizing Inferences from Trials to Target Populations—0
Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach—0
ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction ApplicationsCode0
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models—0
Statistical Agnostic Regression: a machine learning method to validate regression models—0
Optimizing Language Models for Human Preferences is a Causal Inference Problem—0
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive OrdersCode0
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation—0
Causal hybrid modeling with double machine learningCode0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—0
How causal inference concepts can guide research into the effects of climate on infectious diseases—0
Graph Out-of-Distribution Generalization via Causal InterventionCode1
Manipulation Test for Multidimensional RDD—0
Robust agents learn causal world models—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
Conditional Generative Models are Sufficient to Sample from Any Causal Effect EstimandCode0
Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk—0
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study—0
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
PresAIse, A Prescriptive AI Solution for Enterprises—0
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
DoubleMLDeep: Estimation of Causal Effects with Multimodal Data—0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Explaining Text Classifiers with Counterfactual RepresentationsCode0
Continuous Treatment Effects with Surrogate Outcomes—0
How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain—0
Causal Coordinated Concurrent Reinforcement Learning—0
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
SLANG: New Concept Comprehension of Large Language ModelsCode1
Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach—0
Deep Learning-based Group Causal Inference in Multivariate Time-seriesCode0
Modeling Latent Selection with Structural Causal Models—0
Deep Learning With DAGs—0
Valid causal inference with unobserved confounding in high-dimensional settingsCode0
Proximal Causal Inference With Text DataCode0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified