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 876–900 of 1722 papers

TitleStatusHype
Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation—0
Optimizing Language Models for Human Preferences is a Causal Inference Problem—0
Orthogonalized Estimation of Difference of Q-functions—0
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
Orthogonal Random Forest for Causal Inference—0
Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions—0
Test-Time Fairness and Robustness in Large Language Models—0
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows—0
PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation—0
Partial Identification of Causal Effects Using Proxy Variables—0
Partial Identification with Noisy Covariates: A Robust Optimization Approach—0
Partially Intervenable Causal Models—0
Partially Specified Causal Simulations—0
Path Dependent Structural Equation Models—0
Patterns, predictions, and actions: A story about machine learning—0
Perceiving the arrow of time in autoregressive motion—0
Permutation-based Causal Inference Algorithms with Interventions—0
Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning—0
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes—0
Philip G. Wright, directed acyclic graphs, and instrumental variables—0
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty—0
PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals—0
Point Cloud Completion Guided by Prior Knowledge via Causal Inference—0
Political-LLM: Large Language Models in Political Science—0
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees—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