Executive Development Programme in Causal Impact Evaluation
-- ViewingNowThe Executive Development Programme in Causal Impact Evaluation is a certificate course designed to empower professionals with the essential skills to assess and analyze the impact of various business strategies and interventions. This programme is critical for professionals seeking to make data-driven decisions and drive business growth.
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⢠Introduction to Causal Impact Evaluation: Understanding the concept of causal impact, differentiating it from correlation, and the importance of causal inference in decision-making.
⢠Experimental Design: The role of experiments in causal impact evaluation, including randomized controlled trials (RCTs), quasi-experimental designs, and their applications.
⢠Propensity Score Matching: Techniques for reducing selection bias and confounding in observational studies, including propensity score matching, inverse probability weighting, and regression adjustment.
⢠Difference-in-Differences (DiD) Estimation: The DiD method for estimating causal effects, including the parallel trends assumption and applications to policy evaluation.
⢠Instrumental Variables (IV) Estimation: Understanding the concept of instrumental variables, their identification, and estimation techniques, including two-stage least squares (2SLS) and limited information maximum likelihood (LIML).
⢠Regression Discontinuity Design: The use of regression discontinuity design for causal impact evaluation, including sharp and fuzzy regression discontinuity designs.
⢠Sensitivity Analysis: Techniques for assessing the robustness of causal impact estimates to unobserved confounding, including E-values, Rosenbaum bounds, and placebo tests.
⢠Causal Impact Evaluation in Practice: Practical considerations for conducting causal impact evaluations, including data quality, measurement error, and ethical considerations.
⢠Machine Learning Techniques in Causal Inference: Overview of machine learning techniques and their applications in causal inference, including supervised and unsupervised learning, and model selection.
⢠Communicating Causal Impact Findings: Best practices for communicating causal impact findings, including data visualization, writing for different audiences, and stakeholder management.
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