Papers Causal Inference
“Causal Inference” 태그가 달린 논문 2,149편 · 필터 해제
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 InferenceA Causal Inference Approach for Evaluating Diagnostic Tests and AI-Enabled Medical Devices: From Effect Modification to Information-Augmented Decision-Making
Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments. However, unlike treatments, their impact on health outcomes is generally indirect…
Causal InferenceDoubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process. Estimating the caus…
Causal InferenceDiscovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference
The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies. Existing topology generation methods, however, typically learn communication topologies…
Causal InferenceCausal Inference with Unstructured Outcomes
Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives. Modern studies increasingly ask causal questions about outcomes …
Causal InferenceCausal Inference with Unstructured Treatments
Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions. Consider an instructor writing a course description to attr…
Causal InferenceDoubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times. Standar…
Representation LearningCausal InferencePoint CloudsEco3S: Complex Socio-Economic System Simulation via Agent-Based Models
The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM). However, current LLM-based ABM research faces several key challenges: modeling evolving agent-environment interact…
Causal InferenceA cubical formalisation of topos causal models: intervention, sheaf gluing, and the intuitionistic do-calculus
Topos causal models recast causal inference inside a topos: a causal world is a presheaf, an intervention is a characteristic map into the subobject classifier, and reasoning is carried out in the intuitionistic internal…
Causal InferenceCausal Inference for Sequential Settings under Interference and Latent Confounding
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step,…
Causal InferenceRare Concept Generation via Counterfactual Inference in Diffusion Models
Rare concept generation focuses on synthesizing customized images conditioned on text prompts that describe objects with unusual attributes. Previous works failed to align the generated images with rare concepts, resulti…
Image GenerationCausal InferenceContrast-Free ICA and Causal Inference via Wasserstein Distances to the Gaussian
We study the squared $2$-Wasserstein distance to the standard Gaussian as a non-Gaussianity criterion and use it for linear Independent Component Analysis (ICA) and causal discovery in Linear Non-Gaussian Acyclic Models …
Causal InferenceSolution of the Hempel's statistical ambiguity problem and Causal AI
This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions are derived from statistical laws. To avoid such predictions, Carl H…
Causal InferenceDemonstration of the common dual-channel feature decoupling characteristic of front-door mediation causal inference methods in whole-slice image classification
Causal inference using front door intervention and multi-instance learning (MIL) has advanced the analysis of Whole Slide Images (WSI) in digital pathology. These methods adjust feature distributions of subtle evidence s…
Image ClassificationCausal InferenceThe Spectral Structure of Latent Treatment Effects
Identifying heterogeneous treatment effects under unobserved confounding is central in observational causal inference. In proxy models with a discrete latent confounder, prior Synthetic Potential Outcomes (SPO) [Mazaheri…
Causal Inference