Introduction
Going beyond A/B testing
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What do I need to know?
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1. Introduction to Program Evaluation
What is program evaluation?
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Evaluation in data science
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Introduction to causation
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Checklist for evaluations
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2. A/B Testing
What are randomized studies?
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Advantages of A/B testing
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Applications for A/B testing in data science
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Quality checking A/B testing
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Practice A/B testing
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3. Beyond A/B Testing and Randomization
Limitations of A/B testing
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Alternatives to A/B testing
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4. Matching Methods
When to apply matching methods
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Best practices for matching methods
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Advantages of matching methods
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Interpreting results of matching methods
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Practice matching methods
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5. Difference in Difference
When to apply difference in difference
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Best practices for difference in difference
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Advantages of difference in difference
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Interpreting results of difference in difference
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Practice difference in difference
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6. Regression Discontinuity
When to apply regression discontinuity
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Best practices for regression discontinuity
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Advantages of regression discontinuity
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Interpreting results of regression discontinuity
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Practice regression discontinuity
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7. Interrupted Time Series
When to apply interrupted time series
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Best practices for interrupted time series
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Advantages of interrupted time series
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Interpreting results of interrupted time series
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Practice interrupted time series
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Conclusion
Next steps in program evaluation
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Ex_Files_Program_Evaluation_Data_Science.zip
(1.5 MB)