Experimental Design and Causal Inference in R

Discover the difference between correlation and causation in data science. This course will teach you how to design experiments and implement powerful causal inference techniques in R to draw reliable conclusions from your data.
Drawing reliable causal conclusions remains one of the biggest challenges in data science, where confounding variables and selection bias can lead to incorrect interpretations. In this course, Experimental Design and Causal Inference in R, you'll gain the ability to move beyond correlation and establish true causal relationships in your data. First, you'll explore the foundations of experimental design including randomized controlled trials and A/B testing methodologies. Next, you'll discover techniques to handle observational data when randomization isn't possible, including difference-in-differences and propensity score matching. Finally, you'll learn how to implement instrumental variable approaches to address endogeneity problems in complex real-world scenarios. When you're finished with this course, you'll have the skills and knowledge of causal inference needed to design rigorous experiments and draw reliable causal conclusions from both experimental and observational data.
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