Causal Inference
نویسندگان
چکیده
منابع مشابه
Uncertainty in Causal and Counterfactual Inference
We report 4 studies which show that there are systematic quantitative patterns in the way we reason with uncertainty during causal and counterfactual inference. Two specific type of uncertainty uncertainty about facts and about causal relations are explored, and used to model peoples causal inferences (Studies 1-3). We then consider the relationship between causal and counterfactual reason...
متن کاملThe Statistics of Causal Inference: The View from Political Methodology
Many areas of political science focus on causal questions. Evidence from statistical analyses are often used to make the case for causal relationships. While statistical evidence can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on statistica...
متن کاملThe Statistics of Causal Inference: A View from Political Methodology
Many areas of political science focus on causal questions. Evidence from statistical analyses is often used to make the case for causal relationships. While statistical analyses can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on specific st...
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Causal networks (CNs) have been used to construct inference systems for diagnostics and decision making. More recently, Bayesian causal networks (BCNs) and fuzzy causal networks (FCNs) have gained considerable attention and offer an alternative framework for representing structured human knowledge and are used in causal inference in many real-world applications. However, for large systems, it i...
متن کاملEnhanced Fast Causal Network Inference over Event Streams
This paper addresses causal inference and modeling over event streams where data have high throughput, are unbounded, and may arrive out of order. The availability of large amount of data with these characteristics presents several new challenges related to causal modeling, such as the need for fast causal inference operations while ensuring consistent and valid results. There is no existing wo...
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