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Notes on causal inference
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teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Researchers have developed teLLMe, a system to analyze urban driving data for understanding traffic safety and congestion by answering causal questions. The system uses dashcam annotations to build a structured event…
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Researchers developed teLLMe, a system that allows traffic agencies to analyze urban driving data using causal analysis methods. The system helps answer questions about the impact of various factors on traffic density…
teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Researchers have developed teLLMe, a system that helps traffic agencies analyze large volumes of video-derived data for studying safety and congestion by answering causal questions about urban driving, such as 'How…
CausalMix: Data Mixture as Causal Inference for Language Model Training
The paper presents CausalMix, a method that addresses the limitation of existing data mixing methods in Large Language Model (LLM) training by casting it as a causal inference problem. By using causal modeling to…
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