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2. Causal Inference The Mixtape

   mixtape.scunning.com

   This is the online version of Causal Inference: The Mixtape. Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum ...

3. 1 Introduction – Causal Inference The Mixtape

   mixtape.scunning.com/01-introduction

   Causal inference is the leveraging of theory and deep knowledge of institutional details to estimate the impact of events and choices on a given outcome of interest. It is not a new field; humans have been obsessing over causality since antiquity.

4. 7 Instrumental Variables – Causal Inference The Mixtape

   mixtape.scunning.com/07-instrumental_variables

   Let’s consider their model with a single endogenous regressor and a simple constant treatment effect. The causal model of interest here is as before: \[ y=\beta s + \varepsilon \] where \(y\) is some outcome and \(s\) is some endogenous regressor, such as schooling.

5. 9 Difference-in-Differences – Causal Inference The Mixtape

   mixtape.scunning.com/09-difference_in_differences

   The difference-in-differences design is an early quasi-experimental identification strategy for estimating causal effects that predates the randomized experiment by roughly eighty-five years.

6. 8 Panel Data – Causal Inference The Mixtape

   mixtape.scunning.com/08-panel_data

   This chapter focuses on the conditions under which a correlation between \(D\) and \(Y\) reflects a causal effect even with unobserved variables that are correlated with the treatment variable.

7. 4 Potential Outcomes Causal Model – Causal Inference The Mixtape

   mixtape.scunning.com/04-potential_outcomes

   Mill (2010) devised five methods for inferring causation. Those methods were (1) the method of agreement, (2) the method of difference, (3) the joint method, (4) the method of concomitant variation, and (5) the method of residues.

8. 6 Regression Discontinuity – Causal Inference The Mixtape

   mixtape.scunning.com/06-regression_discontinuity

   6.2.6 Inference. As we’ve mentioned, it’s standard practice in the RDD to estimate causal effects using local polynomial regressions. In its simplest form, this amounts to nothing more complicated than fitting a linear specification separately on each side of the cutoff using a least squares regression.

9. 2 Probability and Regression Review – Causal Inference The...

   mixtape.scunning.com/02-probability_and_regression

   In practice, causal inference is based on statistical models that range from the very simple to extremely advanced. And building such models requires some rudimentary knowledge of probability theory, so let’s begin with some definitions.

10. 5 Matching and Subclassification – Causal Inference The Mixtape

    mixtape.scunning.com/05-matching_and_subclassification

    Specifically, insofar as there exists a conditioning strategy that will satisfy the backdoor criterion, then you can use that strategy to identify some causal effect. We now discuss three different kinds of conditioning strategies. They are subclassification, exact matching, and approximate matching. 1.

11. 10 Synthetic Control – Causal Inference The Mixtape

    mixtape.scunning.com/10-synthetic_control

    As discussed in an earlier chapter, randomization inference assigns the treatment to every untreated unit, recalculates the model’s key coefficients, and collects them into a distribution which are then used for inference.