Conceptual Foundations of Causal Inference
Learn the conceptual foundations of causal inference.
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Key Concepts
Review core concepts you need to learn to master this subject
Association vs. Causation
Potential Outcomes Definition
Counterfactual Outcomes Example
Potential Outcomes Notation
Individual Treatment Effects
Average Treatment Effect
Causal Fundamental Problem
Substitutes for Counterfactuals
Association vs. Causation
Association vs. Causation
An association is a relationship between two variables that has a strength or pattern, but is not necessarily causal in nature.
An example of an association is shown in the plot. Because swimming pool sales and forest fires are both high in the summer months (May to August), we might conclude that swimming pools cause forest fires, but really the two variables are similar because they are associated with the heat of summer.
- 1As humans, we are hardwired to look for patterns and identify relationships between things we observe in the world around us. Our brains naturally tend to fill in details and come up with explanati…
- 2Chances are, you or someone you know is superstitious to some extent. Whether it’s wearing a lucky t-shirt to a sporting event or using a favorite pencil and eraser on exams, we believe in supersti…
- 3The second important concept we must learn is counterfactual thinking. Counterfactual thinking is the process of asking, “What WOULD have happened if circumstances were different?” Let’s illustra…
- 4In order to generalize our understanding of the potential outcomes framework, we will now introduce some notation that will be used throughout the rest of this course. - Z represents the treatm…
- 5If we knew both potential observations for every individual, we could use them to estimate several different statistics that summarize the effect of the treatment: - The _individual treatment effe…
- 6Because we can never know both potential outcomes for an individual, we need to use a different method to estimate causal effects. The most accurate way to do this is to use randomization. Rando…
- 7Often, randomization of treatment is unethical. For example, it would be unethical to force hospital patients who don’t like animals to receive therapy animal services. Randomized experiments can a…
- 8So how do we deal with confounders and estimate the ATE when the treatment assignment is not randomized? Let’s return to the therapy animal example once more. Suppose that instead of randomizing …
- 9The last exercise brought up one assumption used throughout causal inference: conditional exchangeability. In this lesson, we will learn a few more assumptions. A second assumption made in causal …
- 10So far, we’ve only focused on the ATE, but there are many other useful estimands we can use to summarize the average causal effect of some intervention or exposure. In many situations, it is not re…
- 11Now that you have some experience with the assumptions needed for causal inference as well as familiarity with a few causal estimands, we need to set up a structured way to apply what you’ve learne…
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