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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 151–175 of 1722 papers

TitleStatusHype
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—0
GST-UNet: Spatiotemporal Causal Inference with Time-Varying ConfoundersCode0
Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect—0
Causal Interpretations in Observational Studies: The Role of Sociocultural Backgrounds and Team Dynamics—0
Practically Effective Adjustment Variable Selection in Causal Inference—0
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees—0
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?Code0
Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation—0
PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units—0
Fixed-Population Causal Inference for Models of Equilibrium—0
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Unfaithful Probability Distributions in Binary Triple of Causality Directed Acyclic Graph—0
STGCN-LSTM for Olympic Medal Prediction: Dynamic Power Modeling and Causal Policy Optimization—0
Reconciling Predictive Multiplicity in PracticeCode0
Detecting clinician implicit biases in diagnoses using proximal causal inferenceCode0
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual InferenceCode0
Philip G. Wright, directed acyclic graphs, and instrumental variables—0
REX: Causal Discovery based on Machine Learning and Explainability techniquesCode1
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Beyond Reward Hacking: Causal Rewards for Large Language Model AlignmentCode4
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference—0
ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments—0
MECD+: Unlocking Event-Level Causal Graph Discovery for Video ReasoningCode1
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—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