Graduate Certificate in Causal Inference with Regression Analysis
-- ViewingNowThe Graduate Certificate in Causal Inference with Regression Analysis is a comprehensive course that equips learners with essential skills in statistical analysis and data interpretation. This program focuses on the importance of causal inference, a critical aspect of data-driven decision making in various industries.
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• Fundamentals of Causal Inference: An introduction to the principles and concepts of causal inference, including the potential outcomes framework and causal graphs.
• Regression Analysis: A review of linear and generalized linear regression models, including assumptions, diagnostics, and model selection.
• Causal Models with Regression: An exploration of how to use regression models to estimate causal effects, including techniques for controlling confounding variables and addressing biases.
• Propensity Score Methods: An introduction to propensity score matching, inverse probability weighting, and other methods for balancing covariates across treatment and control groups.
• Instrumental Variables and IV Regression: An examination of instrumental variables as a means of addressing unmeasured confounding and identifying causal effects in observational studies.
• Difference-in-Differences Estimation: An exploration of the difference-in-differences (DiD) method for estimating causal effects using panel data, with a focus on identifying suitable comparison groups and addressing common challenges.
• Regression Discontinuity Designs: An introduction to regression discontinuity designs (RDD) as a way to estimate causal effects by exploiting sharp or fuzzy discontinuities in the data.
• Advanced Topics in Causal Inference: A survey of advanced topics in causal inference, including causal mediation analysis, principal stratification, and dynamic treatment regimes.
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- ThreeFourHoursPerWeek
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