SOTAVerified

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 201–250 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
Estimation of Treatment Effects in Extreme and Unobserved Data—0
Honesty in Causal Forests: When It Helps and When It Hurts—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
Revolutionizing Clinical Trials: A Manifesto for AI-Driven Transformation—0
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal DiscoveryCode0
Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models—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
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
Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?—0
Machine Mirages: Defining the Undefined—0
Uncovering Bias Mechanisms in Observational Studies—0
Projection Pursuit Density Ratio Estimation—0
Recover Experimental Data with Selection Bias using Counterfactual Logic—0
Data Fusion for Partial Identification of Causal Effects—0
Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis—0
Preventing Spurious Interactions: A New Inductive Bias for Accurate Treatment Effect EstimationCode0
A Synthetic Business Cycle Approach to Counterfactual Analysis with Nonstationary Macroeconomic Data—0
MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network—0
Debiased Ill-Posed Regression—0
Model Agnostic Differentially Private Causal Inference—0
Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement—0
Causal Discovery in Dynamic Fading Wireless Networks—0
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation—0
BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge BasesCode0
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine—0
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding—0
On the Out-of-Distribution Generalization of Self-Supervised LearningCode0
Towards a Science of Causal Interpretability in Deep Learning for Software Engineering—0
Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in MobilityCode0
PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals—0
Cooperative Causal GraphSAGE—0
Causal Cartographer: From Mapping to Reasoning Over Counterfactual WorldsCode0
Assimilative Causal Inference—0
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time InsightCode0
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI—0
Machine learning the first stage in 2SLS: Practical guidance from bias decomposition and simulation—0
Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference—0
Show:102550
← PrevPage 5 of 35Next →
All datasetsIHDPJobsIDHP

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