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

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Showing 251–300 of 1722 papers

TitleStatusHype
LLMScan: Causal Scan for LLM Misbehavior DetectionCode0
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis—0
Accounting for Missing Covariates in Heterogeneous Treatment Estimation—0
Are Bayesian networks typically faithful?—0
Differentially Private Covariate Balancing Causal Inference—0
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
Counterfactual Generative Modeling with Variational Causal InferenceCode1
Do LLMs Have the Generalization Ability in Conducting Causal Inference?Code0
A Practical Approach to Causal Inference over TimeCode0
DAG-aware Transformer for Causal Effect EstimationCode0
Learning Representations of Instruments for Partial Identification of Treatment EffectsCode0
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Causal Image Modeling for Efficient Visual UnderstandingCode1
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference—0
Medical Image Quality Assessment based on Probability of Necessity and Sufficiency—0
Counterfactual Causal Inference in Natural Language with Large Language ModelsCode1
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning—0
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
Causal Inference Tools for a Better Evaluation of Machine Learning—0
Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening—0
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding SampleCode0
Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments—0
Interpretable, multi-dimensional Evaluation Framework for Causal Discovery from observational i.i.d. DataCode0
Using Deep Autoregressive Models as Causal Inference Engines—0
Detecting and Measuring Confounding Using Causal Mechanism ShiftsCode0
Towards Representation Learning for Weighting Problems in Design-Based Causal InferenceCode0
Linear Contextual Bandits with Interference—0
Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification—0
CauSkelNet: Causal Representation Learning for Human Behaviour Analysis—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants—0
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional IndependenciesCode0
Mining Causality: AI-Assisted Search for Instrumental Variables—0
A Way to Synthetic Triple Difference—0
Performance of Cross-Validated Targeted Maximum Likelihood EstimationCode0
Causal Inference with Large Language Model: A Survey—0
Unconditional Randomization Tests for Interference—0
Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence—0
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks—0
Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments—0
Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation—0
K-Fold Causal BART for CATE Estimation—0
Towards Measuring Sell Side Outcomes in Buy Side Marketplace Experiments using In-Experiment Bipartite Graph—0
Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mapping—0
TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations—0
LinFusion: 1 GPU, 1 Minute, 16K ImageCode3
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
Higher order definition of causality by optimally conditioned transfer entropy—0
Causal Reasoning in Software Quality Assurance: A Systematic Review—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—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