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 1–50 of 1722 papers

TitleStatusHype
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelsCode5
Beyond Reward Hacking: Causal Rewards for Large Language Model AlignmentCode4
pgmpy: A Python Toolkit for Bayesian NetworksCode4
LinFusion: 1 GPU, 1 Minute, 16K ImageCode3
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal ReasoningCode3
DoWhy: An End-to-End Library for Causal InferenceCode3
CausalML: Python Package for Causal Machine LearningCode3
Do we actually understand the impact of renewables on electricity prices? A causal inference approachCode2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
Causal Evaluation of Language ModelsCode2
Vision-and-Language Navigation via Causal LearningCode2
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality AssessmentCode2
Applied Causal Inference Powered by ML and AICode2
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular DataCode2
Out-of-sample scoring and automatic selection of causal estimatorsCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Unifying Pairwise Interactions in Complex DynamicsCode2
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same EndCode2
Unbiased Scene Graph Generation from Biased TrainingCode2
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution ShiftsCode1
DeCaFlow: A Deconfounding Causal Generative ModelCode1
Causal Inference for Qualitative OutcomesCode1
REX: Causal Discovery based on Machine Learning and Explainability techniquesCode1
MECD+: Unlocking Event-Level Causal Graph Discovery for Video ReasoningCode1
Eliciting Causal Abilities in Large Language Models for Reasoning TasksCode1
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal DiscoveryCode1
CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public EventsCode1
Language Models as Causal Effect GeneratorsCode1
Language Agents Meet Causality -- Bridging LLMs and Causal World ModelsCode1
Counterfactual Generative Modeling with Variational Causal InferenceCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Causal Image Modeling for Efficient Visual UnderstandingCode1
Counterfactual Causal Inference in Natural Language with Large Language ModelsCode1
See or Guess: Counterfactually Regularized Image CaptioningCode1
General targeted machine learning for modern causal mediation analysisCode1
Multi-task Heterogeneous Graph Learning on Electronic Health RecordsCode1
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication RecommendationCode1
Discriminative and Consistent Representation DistillationCode1
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark FrameworkCode1
Learning Divergence Fields for Shift-Robust Graph RepresentationsCode1
A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of PovertyCode1
A Brief Introduction to Causal Inference in Machine LearningCode1
Disentangling ID and Modality Effects for Session-based RecommendationCode1
The Causal Chambers: Real Physical Systems as a Testbed for AI MethodologyCode1
C-XGBoost: A tree boosting model for causal effect estimationCode1
Semi-Supervised Learning for Deep Causal Generative ModelsCode1
CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity LearningCode1
Language Models Represent Beliefs of Self and OthersCode1
Graph Out-of-Distribution Generalization via Causal InterventionCode1
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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