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Skip to Search Results- 2Causal Inference
- 1 Causal Effect Estimation
- 1 Treatment Effect Estimation
- 1Bayesian Network
- 1Counterfactual Regression
- 1Data Reconciliation
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Fall 2022
As one of the main tasks in studying causality, the goal of Causal Inference is to determine "whether" (and perhaps "how much") the value of a certain variable (i.e., the effect) would change, had another specified variable (i.e., the cause) changed its value. A prominent example is the...
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Probabilistic Graphical Models for Data Reconciliation and Causal Inference in Process Data Analytics
DownloadSpring 2021
Data reconciliation methods play an important role in minimizing the measurement error and gross error that are present in the process data with respect to the process model. On the other hand, causal analysis helps in determining the relationship among the process variables from the data. It is...