Causal Data Science: Making Decisions Over Time

In data science, observing that two events happen together over time does not mean one caused the other. To make strategic decisions that truly drive impact, you must understand the underlying causal mechanisms within your sequential data. This course teaches you how to move beyond simple correlation and use causal information to guide decision-making over time. You will learn to map out causal relationships, analyze temporal effects, and predict the real-world outcomes of your interventions. What you'll learn: - Understand the foundational differences between correlation and causation in time-series data. - Map complex systems using Directed Acyclic Graphs (DAGs) and Structural Causal Models. - Apply causal estimation techniques to measure the impact of decisions over time. - Analyze sequential and temporal data to identify true causal pathways rather than coincidental trends. - Evaluate policy changes and interventions using modern causal frameworks. The course begins with core terminology and foundational causal concepts before guiding you through practical frameworks for analyzing temporal data and modeling decisions. You will progress from basic definitions to reading and constructing causal diagrams for real-world scenarios through written explanations and structured examples. This course is designed for aspiring data analysts, product managers, and beginner data scientists who want to make more robust, evidence-based recommendations without needing advanced mathematical prerequisites. Start reading today to unlock the power of causal thinking in your data analysis.