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

Showing 125 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
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Benchmark Results

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