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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 1722 papers

TitleStatusHype
Learning Causally Predictable Outcomes from Psychiatric Longitudinal DataCode0
Linear-Time Primitives for Algorithm Development in Graphical Causal Inference—0
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond—0
Honesty in Causal Forests: When It Helps and When It Hurts—0
Estimation of Treatment Effects in Extreme and Unobserved Data—0
Rethinking Distributional IVs: KAN-Powered D-IV-LATE & Model Choice—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
Directed Acyclic Graph Convolutional Networks—0
Foundation Models for Causal Inference via Prior-Data Fitted Networks—0
From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat ExplanationsCode0
Correlation vs causation in Alzheimer's disease: an interpretability-driven study—0
STOAT: Spatial-Temporal Probabilistic Causal Inference Network—0
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal DiscoveryCode0
Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation—0
Quantile-Optimal Policy Learning under Unmeasured Confounding—0
Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis—0
Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models—0
Causal Effect Identification in lvLiNGAM from Higher-Order CumulantsCode0
What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness—0
N^2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion—0
AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving—0
Machine Mirages: Defining the Undefined—0
Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?—0
Uncovering Bias Mechanisms in Observational Studies—0
Projection Pursuit Density Ratio Estimation—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