Causal Inference
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Benchmarks
Most implemented
Unbiased Scene Graph Generation from Biased Training
Causal Effect Inference with Deep Latent-Variable Models
DoWhy: An End-to-End Library for Causal Inference
Adapting Neural Networks for the Estimation of Treatment Effects
Adapting Text Embeddings for Causal Inference
Double/Debiased Machine Learning for Treatment and Causal Parameters
Papers
Pragmatic DML with AI-Learned Representations
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is valid and develop a practical framework for…
Representation LearningCausal InferenceCovariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult. Double/debiased machine learning (DML) facili…
Causal InferenceCausal Foundation Models
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a comp…
Causal InferenceLearning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning
Complete your ad view and grab a 5-cent bonus! In incentivized advertising, a platform promises users a bonus before observing downstream ad revenue, encouraging them to click and complete ads. It must balance the incent…
Reinforcement LearningCausal InferenceOffline RL4DStreamCtrl: Interactive Video Generation with Online 4D Control
Generative video models now synthesize footage nearly indistinguishable from reality. Their promise as interactive tools hinges on fine-grained control of how objects and the camera move over time, yet each existing appr…
Video GenerationCausal InferenceLet Time Tell: Identification and Gaussian Process Estimation for Interrupted Time Series
We study causal inference in interrupted time series designs where a treatment affects every unit simultaneously, so that the contemporaneous controls used by difference-in-differences and synthetic control are unavailab…
Causal Inference