We wrap up the Causal Inference Bootcamp by looking at how the different techniques we've talked about can work together, and what choices you might have to make as you start your analysis of data.
This module covers the basic ideas and issues in determining causation.
This module includes all of the experiments from the Causal Inference Bootcamp and discusses how treatments and controls can exhibit causal effects.
This module of the Causal Inference Bootcamp explains the basic concepts of IV analysis and how social scientists use it to indirectly find causality in their data.
We cover the main ways that researchers take limited data and make choices about their assumptions and expectations in order to create models of behavior for when experiments are extremely difficult or even impossible.
This module walks you through the basic concepts of panel data, referencing a few examples and discussing the benefits and potential trouble spots.
This module includes videos on what a regression is, what it will look like in a paper, and the benefits and pitfalls of using and interpreting regressions for causal inference.
In its simplest form, regression discontinuity is a pretest-posttest program-comparison group strategy. It can be a useful method for determining whether a program or treatment is effective.
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