Master Causal inference Training Courses

Bayesian Causal Inference Training Course provides participants with the skills to implement Bayesian methods in causal inference using real-world applications in health research, economics, policy evaluation, marketing, and AI systems.

Causal Discovery from Observational Data Training Course provides advanced training in conducting ethically sound and methodologically robust research on sensitive issues, with a special focus on using causal discovery from observational data.

Causal Inference with Difference-in-Differences (DiD) Training Course is designed for researchers, analysts, economists, and professionals seeking to master one of the most widely-used quasi-experimental research designs.

Contribution Analysis for Causal Inference in M&E Training Course is designed to strengthen causal inference and enhance evidence-based decision-making.

Difference-in-Differences (DiD) Analysis Training Course equips participants with advanced analytical skills to identify treatment effects, control for confounding variables, and interpret results with precision

Econometrics in Advanced Causal Inference Techniques Training Course is a cutting-edge, expert-level program designed to equip participants with the skills to identify, estimate, and interpret complex causal relationships using real-world data.

Experimental Design and Causal Inference for Applied Research Training Course equips participants with cutting-edge methodologies and statistical tools to establish causality, design robust experiments, and apply advanced analytical frameworks to real-world scenarios.

Experimental Designs in Evaluation Training Course empowers participants to design, implement, and analyze experiments that accurately measure program effectiveness, assess causal relationships, and optimize interventions.

Experimental Econometrics in Design and Analysis Training Course equips participants with essential tools to design robust economic experiments, apply advanced econometric techniques, and interpret complex datasets for real-world policy analysis.

Propensity Score Matching (PSM) for Quasi-Experimental Designs Training Course is designed to equip researchers, data analysts, and policy evaluators with advanced skills to address selection bias and improve the credibility of their results.

Quasi-Experimental Methods in M&E Training Course offers a deep dive into quasi-experimental designs, covering methodologies such as difference-in-differences, propensity score matching, regression discontinuity, and interrupted time series.

Training Course on Causal Inference for Data Scientists equips Data Scientists, Analysts, and Researchers with the cutting-edge statistical methods and practical frameworks necessary to rigorously distinguish between observed associations and genuine causal impacts.