Papers Counterfactual Inference
“Counterfactual Inference” 태그가 달린 논문 124편 · 필터 해제
Going from a Representative Agent to Counterfactuals in Combinatorial Choice
We study decision-making problems where data comprises points from a collection of binary polytopes, capturing aggregate information stemming from various combinatorial selection environments. We propose a nonparametric …
counterfactualCounterfactual InferenceDecision MakingAdaptively-weighted Nearest Neighbors for Matrix Completion
In this technical note, we introduce and analyze AWNN: an adaptively weighted nearest neighbor method for performing matrix completion. Nearest neighbor (NN) methods are widely used in missing data problems across multip…
counterfactualCounterfactual InferenceMatrix CompletionRecommendation SystemsCounterfactual Inference for Eliminating Sentiment Bias in Recommender Systems
Recommender Systems (RSs) aim to provide personalized recommendations for users. A newly discovered bias, known as sentiment bias, uncovers a common phenomenon within Review-based RSs (RRSs): the recommendation accuracy …
counterfactualCounterfactual InferenceRecommendation SystemsBeyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation
Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typically model the dialogue context as a wh…
Conversational RecommendationcounterfactualCounterfactual InferenceDisentanglement+3Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypothetical scenarios to examine the effects of …
Causal DiscoverycounterfactualCounterfactual InferenceCounterfactual Inference under Thompson Sampling
Recommender systems exemplify sequential decision-making under uncertainty, strategically deciding what content to serve to users, to optimise a range of potential objectives. To balance the explore-exploit trade-off suc…
Causal InferencecounterfactualCounterfactual InferenceDecision Making+4Counterfactual experience augmented off-policy reinforcement learning
Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enhances the agent's reasoning and planning …
counterfactualCounterfactual InferenceDensity EstimationModel-based Reinforcement Learning+2MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognition
Surgical phase recognition from video enables various downstream applications. Transformer-based sliding window approaches have set the state-of-the-art by capturing rich spatial-temporal features. However, while transfo…
counterfactualCounterfactual InferenceSurgical phase recognitionRobust Counterfactual Inference in Markov Decision Processes
This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make counterfactuals identifiable. However, th…
counterfactualCounterfactual InferenceCounterBench: A Benchmark for Counterfactuals Reasoning in Large Language Models
Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this paper, we evaluate the performance of large language models (LLMs) in co…
Commonsense Causal ReasoningcounterfactualCounterfactual InferenceCounterfactual ReasoningControllable Sequence Editing for Biological and Clinical Trajectories
Conditional generation models for longitudinal sequences can generate new or modified trajectories given a conditioning input. While effective at generating entire sequences, these models typically lack control over the …
counterfactualCounterfactual InferenceCounterfactual ReasoningCausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inference
Physical and optical factors interacting with sensor characteristics create complex image degradation patterns. Despite advances in deep learning-based super-resolution, existing methods overlook the causal nature of deg…
Causal InferencecounterfactualCounterfactual InferenceImage Restoration+1MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
Video causal reasoning aims to achieve a high-level understanding of videos from a causal perspective. However, it exhibits limitations in its scope, primarily executed in a question-answering paradigm and focusing on br…
Causal DiscoveryCausal InferencecounterfactualCounterfactual Inference+3Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize ac…
counterfactualCounterfactual InferenceFull reference image quality assessmentFull-Reference Image Quality Assessment+1Multimodal Sentiment Analysis Based on Causal Reasoning
With the rapid development of multimedia, the shift from unimodal textual sentiment analysis to multimodal image-text sentiment analysis has obtained academic and industrial attention in recent years. However, multimodal…
Causal InferencecounterfactualCounterfactual InferenceMultimodal Sentiment Analysis+2Navigating Spatial Inequities in Freight Truck Crash Severity via Counterfactual Inference in Los Angeles
Freight truck-related crashes pose significant challenges, leading to substantial economic losses, injuries, and fatalities, with pronounced spatial disparities across different regions. This study adopts a transport geo…
counterfactualCounterfactual InferenceDo LLMs Have the Generalization Ability in Conducting Causal Inference?
In causal inference, generalization capability refers to the ability to conduct causal inference methods on new data to estimate the causal-effect between unknown phenomenon, which is crucial for expanding the boundaries…
Causal InferencecounterfactualCounterfactual InferenceCounterfactual Causal Inference in Natural Language with Large Language Models
Causal structure discovery methods are commonly applied to structured data where the causal variables are known and where statistical testing can be used to assess the causal relationships. By contrast, recovering a caus…
ArticlesCausal InferencecounterfactualCounterfactual InferenceMitigating Propensity Bias of Large Language Models for Recommender Systems
The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analyses of items) generated by these models.…
counterfactualCounterfactual InferenceRecommendation SystemsModality-Balanced Learning for Multimedia Recommendation
Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use of multimodal information is expected to…
Collaborative FilteringcounterfactualCounterfactual InferenceKnowledge Distillation+2