This course introduces the fundamental concepts and methods of causal inference, the science of determining cause-and-effect relationships from data. Students will learn about causality, confounding, experimental and observational study designs, and modern causal inference techniques such as propensity score matching, difference-in-differences, instrumental variables, and causal diagrams.
Welcome to the first episode of our Causal Inference course! This introductory session lays the groundwork for understanding not just *what* is happening in your data, but *why*. We'll explore the crucial distinction between prediction, the focus of …Welcome to the first episode of our Causal Inference course! This introductory session lays the groundwork for understanding not just *what* is happening in your data, but *why*. We'll explore the crucial distinction between prediction, the focus of traditional machine learning, and causal reasoning, which seeks to understand the effects of actions and interventions. You'll be introduced to a powerful conceptual framework, the 'Ladder of Causation,' to help structure your thinking about causal questions. We'll also discuss the 'Fundamental Problem of Causal Inference,' a core challenge that motivates the entire field. This episode will equip you with the foundational mindset needed to tackle the more advanced methods we'll cover later, moving you from simply observing patterns to asking 'what if?'.
Welcome to the second episode of our Causal Inference course. In this session, we delve into the fundamental concept of Causality itself. Building on our introduction to causal inference, we'll explore what it truly means for one event to cause anoth…Welcome to the second episode of our Causal Inference course. In this session, we delve into the fundamental concept of Causality itself. Building on our introduction to causal inference, we'll explore what it truly means for one event to cause another, moving beyond simple associations. We will discuss the philosophical foundations, including David Hume's problem of induction, and introduce the crucial 'interventionist' perspective. You will also learn to distinguish between different types of causes, such as necessary and sufficient causes, providing you with a more nuanced framework for thinking about cause-and-effect relationships. This episode lays the essential groundwork for the advanced methods we will cover later.
Welcome to the third episode of our Causal Inference course! Building on our understanding of causality, this episode tackles one of the most fundamental principles in statistics and science: 'Correlation does not imply causation.' We will explore wh…Welcome to the third episode of our Causal Inference course! Building on our understanding of causality, this episode tackles one of the most fundamental principles in statistics and science: 'Correlation does not imply causation.' We will explore what correlation is and, more importantly, why the simple fact that two trends move together doesn't prove that one causes the other. Using relatable examples, from ice cream sales and shark attacks to firefighters and fire damage, we will uncover the common logical traps people fall into. This crucial lesson will highlight the dangers of jumping to conclusions and set the stage for the more advanced methods we'll use later in the course to uncover true causal relationships.
Welcome to the fourth episode of our Causal Inference course! Building on our understanding of causality and the principle that correlation does not imply causation, we now dive into one of the biggest challenges in establishing cause-and-effect: **C…Welcome to the fourth episode of our Causal Inference course! Building on our understanding of causality and the principle that correlation does not imply causation, we now dive into one of the biggest challenges in establishing cause-and-effect: **Confounding**. This episode will introduce you to the concept of a 'lurking' third variable that can distort the relationship between two other variables, creating a misleading association. We will define the three essential properties of a confounder, explore real-world examples from medicine and social sciences, and explain why failing to account for confounding can lead to completely wrong conclusions. This foundational knowledge is crucial for appreciating the methods we will discuss in future episodes, such as randomized controlled trials.
Welcome to the fifth episode of our Causal Inference course! In this session, we explore the 'gold standard' for establishing cause-and-effect: the Randomized Controlled Trial (RCT). Building on our understanding of confounding, we'll dissect how RCT…Welcome to the fifth episode of our Causal Inference course! In this session, we explore the 'gold standard' for establishing cause-and-effect: the Randomized Controlled Trial (RCT). Building on our understanding of confounding, we'll dissect how RCTs work, focusing on the power of randomization to create comparable groups. You will learn about the essential roles of treatment and control groups and how this experimental design allows us to isolate and measure the true effect of an intervention. This episode provides the foundational understanding of experimental design before we venture into methods for drawing causal conclusions from non-experimental data in later episodes.
Welcome to the sixth episode of our Causal Inference course. This time, we step away from the experimental ideal to explore the world of Observational Studies. Where Randomized Controlled Trials (RCTs) actively assign treatments, observational studie…Welcome to the sixth episode of our Causal Inference course. This time, we step away from the experimental ideal to explore the world of Observational Studies. Where Randomized Controlled Trials (RCTs) actively assign treatments, observational studies passively observe the world as it is. We will define what these studies are, contrast them with RCTs, and delve into their primary challenge: confounding. You'll learn about the main types of observational designs—cohort, case-control, and cross-sectional—and understand their unique strengths and weaknesses. This episode builds a critical foundation for why more advanced techniques, which we will cover later, are necessary to draw causal conclusions from non-experimental data.
Welcome to Episode 7 of our Causal Inference course! In previous episodes, we established that Randomized Controlled Trials (RCTs) are the gold standard for causal inference, but what can we do when they aren't feasible? This episode introduces Prope…Welcome to Episode 7 of our Causal Inference course! In previous episodes, we established that Randomized Controlled Trials (RCTs) are the gold standard for causal inference, but what can we do when they aren't feasible? This episode introduces Propensity Score Matching (PSM), a powerful technique for analyzing observational data. You'll learn how PSM helps us mimic the conditions of an RCT by balancing covariates between treatment and control groups. We'll explore what a propensity score is, how it's calculated, and the different ways we can use it to match individuals. By the end, you'll understand how PSM addresses confounding and selection bias, moving us closer to making causal claims from non-experimental data.
Welcome to the eighth episode of our Causal Inference course! This time, we explore Difference in Differences (DiD), a popular and intuitive quasi-experimental method. DiD is a powerful tool for estimating the causal effects of interventions using ob…Welcome to the eighth episode of our Causal Inference course! This time, we explore Difference in Differences (DiD), a popular and intuitive quasi-experimental method. DiD is a powerful tool for estimating the causal effects of interventions using observational data when a randomized controlled trial isn't possible. We'll break down the logic of how it compares changes over time between a treatment and a control group to isolate the treatment's impact. You will learn about its core mechanism, its single most important assumption—the parallel trends assumption—and understand its strengths and limitations. This episode will equip you to recognize situations where DiD can provide credible causal estimates from real-world data.
Welcome to the ninth episode of our Causal Inference course! In previous lessons, we've explored methods like Propensity Score Matching and Difference-in-Differences to handle confounding variables we can observe. But what happens when the confounder…Welcome to the ninth episode of our Causal Inference course! In previous lessons, we've explored methods like Propensity Score Matching and Difference-in-Differences to handle confounding variables we can observe. But what happens when the confounder is hidden or unmeasurable, like innate 'ability' or 'motivation'? This episode introduces Instrumental Variables (IV) estimation, a powerful and clever technique to uncover causal effects even in the presence of such unobserved confounding. You will learn what an instrumental variable is, the two crucial conditions it must satisfy—relevance and the exclusion restriction—and the two-stage logic that allows it to isolate the causal impact of a treatment. By the end, you'll understand how IV provides a solution to one of the toughest problems in causal inference.
Welcome to Episode 10 of our Causal Inference course! This episode introduces Regression Discontinuity Design (RDD), a powerful quasi-experimental method. You will learn how RDD leverages sharp cutoffs in assignment rules—like a minimum test score fo…Welcome to Episode 10 of our Causal Inference course! This episode introduces Regression Discontinuity Design (RDD), a powerful quasi-experimental method. You will learn how RDD leverages sharp cutoffs in assignment rules—like a minimum test score for a scholarship—to create a natural experiment. We'll explore the core intuition behind RDD, distinguishing it from other observational methods by showing how it mimics a randomized controlled trial for subjects right around the threshold. By the end, you will understand the key assumptions that make RDD a credible tool for estimating causal effects, its main variations (Sharp vs. Fuzzy), and its real-world applications in policy, economics, and healthcare.
Welcome to Episode 11 of the Causal Inference course. In this episode, we introduce Directed Acyclic Graphs (DAGs), a powerful visual framework for mapping out our causal assumptions. You will learn what DAGs are, what their components signify, and h…Welcome to Episode 11 of the Causal Inference course. In this episode, we introduce Directed Acyclic Graphs (DAGs), a powerful visual framework for mapping out our causal assumptions. You will learn what DAGs are, what their components signify, and how they provide a formal language for reasoning about complex causal systems. We will explore how DAGs make abstract concepts like confounding concrete by identifying 'backdoor paths' and how they warn us against common pitfalls like selection bias through structures known as 'colliders'. This episode will equip you with the foundational knowledge to translate your understanding of a problem into a formal causal model, guiding your choice of statistical methods and variables for analysis.
Welcome to episode 12 of our Causal Inference course! This session delves into the fascinating concept of Counterfactual Thinking, the mental framework of asking “what if?” that underpins modern causal analysis. We will explore how this idea is forma…Welcome to episode 12 of our Causal Inference course! This session delves into the fascinating concept of Counterfactual Thinking, the mental framework of asking “what if?” that underpins modern causal analysis. We will explore how this idea is formalized through the Potential Outcomes Framework, helping us define what a causal effect truly is. You'll learn how counterfactuals connect to concepts you're already familiar with, like Randomized Controlled Trials and confounding in observational studies. By the end, you will understand why thinking about unobserved, alternative realities is not just a philosophical exercise but a crucial and practical tool for estimating cause-and-effect relationships from data.
Welcome to episode 13 of our Causal Inference course. In this session, we introduce a cornerstone concept: the Average Treatment Effect, or ATE. We'll explore how the ATE provides a single, powerful number to summarize the overall impact of an interv…Welcome to episode 13 of our Causal Inference course. In this session, we introduce a cornerstone concept: the Average Treatment Effect, or ATE. We'll explore how the ATE provides a single, powerful number to summarize the overall impact of an intervention across an entire population. Building on our understanding of counterfactuals and randomized controlled trials, you will learn the formal definition of the ATE and why randomization is the gold standard for estimating it. We will also differentiate the ATE from more specific measures like the Average Treatment Effect on the Treated (ATT), discussing when and why these different quantities are important. This episode will equip you with the foundational language for quantifying causal impact.
Welcome to Episode 14 of our Causal Inference course! In this session, we unravel the mysteries of Simpson's Paradox, a statistical phenomenon where a trend that appears in different groups of data disappears or even reverses when these groups are co…Welcome to Episode 14 of our Causal Inference course! In this session, we unravel the mysteries of Simpson's Paradox, a statistical phenomenon where a trend that appears in different groups of data disappears or even reverses when these groups are combined. We will explore classic examples to understand how this counterintuitive situation arises and connect it directly to the crucial concept of confounding, which we've discussed previously. You will learn that resolving the paradox isn't a simple statistical choice but requires deep causal reasoning. By the end of this episode, you'll be able to identify potential instances of Simpson's Paradox and understand why asking 'why' is essential before drawing conclusions from data.
Welcome to the final episode of our Causal Inference course! Having mastered how to identify *if* a cause has an effect and *how much* of an effect it has, we now turn to the crucial questions of *how* and *why*. This episode introduces mediation ana…Welcome to the final episode of our Causal Inference course! Having mastered how to identify *if* a cause has an effect and *how much* of an effect it has, we now turn to the crucial questions of *how* and *why*. This episode introduces mediation analysis, a powerful statistical technique for understanding the mechanisms or pathways through which a cause produces its effect. We will explore how to decompose a total causal effect into its direct and indirect components, using concepts like Directed Acyclic Graphs that you've learned previously. By the end, you'll be able to look beyond the overall effect and uncover the intricate story of causality that unfolds between a treatment and an outcome, providing deeper insights for policy and decision-making.