Global Certificate in Causal Inference Applications
-- ViewingNowThe Global Certificate in Causal Inference Applications is a comprehensive course designed to equip learners with essential skills in causal inference, a highly sought-after skill in various industries. This course is critical for professionals who want to make data-driven decisions, understand the impact of interventions, and evaluate policies in areas such as healthcare, economics, and social sciences.
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⢠Introduction to Causal Inference: Foundational concepts, potential outcomes framework, causal effects, and identifying valid causal estimands.
⢠Propensity Score Matching: Overview, propensity score estimation, balance checking, and implementing propensity score matching.
⢠Difference-in-Differences (DID) Estimation: Parallel trends assumption, event studies, synthetic control methods, and DID applications.
⢠Regression Discontinuity Designs (RDD): Continuous and discrete treatment assignment, fuzzy RDD, estimating treatment effects, and practical RDD applications.
⢠Instrumental Variables (IV) Analysis: IV identification, two-stage least squares (2SLS), limited information maximum likelihood (LIML), and weak instruments.
⢠Econometric Evaluation of Programs and Policies: Counterfactual analysis, policy evaluation metrics, quasi-experimental approaches, and causal forest algorithms.
⢠Machine Learning Techniques for Causal Inference: Overview of machine learning, supervised and unsupervised learning, and model-based and model-agnostic methods.
⢠Causal Inference in Observational Data: Confounding bias, collider bias, backdoor criteria, and frontdoor criteria.
⢠Causal Inference Ethics: Ethical considerations, responsible data use, and potential harms of causal inference.
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