├── .DS_Store ├── .gitignore ├── 1 Foundations.pdf ├── 2 Some Causal Models.pdf ├── 3 Inference.pdf ├── 4 Best Practices (WIP).pdf ├── 5 HTE Models 2023 update.pdf ├── 5 HTE Models.pdf ├── 6 Panel Models.pdf ├── 9 Arguable Validation.pdf ├── Notebooks ├── .DS_Store ├── .ipynb_checkpoints │ ├── 1 Foundations 22dec2022-checkpoint.ipynb │ ├── 1 Foundations-checkpoint.ipynb │ ├── 1 Foundations10aug2021-checkpoint.ipynb │ ├── 4 Best Practices - Coefficient Stability-checkpoint.ipynb │ ├── 4 Best Practices - Outliers-checkpoint.ipynb │ ├── 4 Best Practices - Trimming-checkpoint.ipynb │ ├── 4 Best Practices-checkpoint.ipynb │ ├── 5 HTE Models-checkpoint.ipynb │ ├── 6 - Panel Data-checkpoint.ipynb │ ├── 9 - Synthetic Control Analysis-checkpoint.ipynb │ ├── 9 Synthetic Control - CPS Data -checkpoint.ipynb │ └── 99 Surrogate Models-checkpoint.ipynb ├── 1 Foundations 22dec2022.ipynb ├── 1 Foundations.ipynb ├── 1 Foundations10aug2021.ipynb ├── 4 Best Practices - Coefficient Stability.ipynb ├── 4 Best Practices - Outliers.ipynb ├── 4 Best Practices - Trimming.ipynb ├── 5 HTE Models.ipynb ├── 6 - Panel Data.ipynb ├── 9 - Synthetic Control Analysis.ipynb ├── 9 Synthetic Control - CPS Data ├── 9 Synthetic Control - CPS Data .ipynb └── 99 Surrogate Models.ipynb ├── OtherMaterial ├── .ipynb_checkpoints │ ├── GradientDescent Example-checkpoint.ipynb │ ├── Identifying Variation for Estimates-checkpoint.ipynb │ ├── LaGrangian Optimization-checkpoint.ipynb │ ├── OLS via Gradient Descent and Matrix Inversion-checkpoint.ipynb │ └── Two Part Hurdle Model-checkpoint.ipynb ├── Causal ML DML and Otherwise │ ├── .ipynb_checkpoints │ │ ├── All You Need Are Propensity Scores-checkpoint.ipynb │ │ ├── Causal Inference versus Prediction in Linear Regression Models-checkpoint.ipynb │ │ ├── Quality of Residualization-checkpoint.ipynb │ │ ├── Relationship between OLS and DML-checkpoint.ipynb │ │ └── [WiP] DML and Detecting Instruments-checkpoint.ipynb │ ├── All You Need Are Propensity Scores.ipynb │ ├── Causal Inference versus Prediction in Linear Regression Models.ipynb │ ├── Quality of Residualization.ipynb │ ├── Relationship between OLS and DML.ipynb │ └── [WiP] DML and Detecting Instruments.ipynb ├── Experimental Analysis and Deep Diving Treatment Effects │ ├── .ipynb_checkpoints │ │ ├── Continuous Treatments with Repeated Actions-checkpoint.ipynb │ │ ├── Experiment Duration and Variance-checkpoint.ipynb │ │ ├── Heterogeneity in Noisy Aggregate Estimates-checkpoint.ipynb │ │ ├── Infra and Extra Marginal Treatment Effects-checkpoint.ipynb │ │ ├── Multiple Treatments-checkpoint.ipynb │ │ ├── Precision Benefits from Interpolating and Extrapolating from Experiments-checkpoint.ipynb │ │ ├── Propensity Score Known-checkpoint.ipynb │ │ ├── Spillovers in Treatments Trends-checkpoint.ipynb │ │ └── Stratification-checkpoint.ipynb │ ├── Continuous Treatments with Repeated Actions.ipynb │ ├── Experiment Duration and Variance.ipynb │ ├── Heterogeneity in Noisy Aggregate Estimates.ipynb │ ├── Infra and Extra Marginal Treatment Effects.ipynb │ ├── Multiple Treatments.ipynb │ ├── Precision Benefits from Interpolating and Extrapolating from Experiments.ipynb │ ├── Propensity Score Known.ipynb │ ├── Spillovers in Treatments Trends.ipynb │ └── Stratification.ipynb ├── General Points about Estimating Treatment Effect Models │ ├── .ipynb_checkpoints │ │ ├── ATE vs ATT vs ATU-checkpoint.ipynb │ │ ├── Bad Control of Intermediate Outcomes-checkpoint.ipynb │ │ ├── Bias when Unconfoundedness is not Met-checkpoint.ipynb │ │ ├── Bootstrap vs Jackknife-checkpoint.ipynb │ │ ├── Clustering Standard Errors-checkpoint.ipynb │ │ ├── Inference on Winners-checkpoint.ipynb │ │ ├── Logarithm Transformations-checkpoint.ipynb │ │ ├── OutliersDemonstration-checkpoint.ipynb │ │ ├── PostEstimationPower-checkpoint.ipynb │ │ ├── Quantile Regression Example-checkpoint.ipynb │ │ ├── Trimming Propensity Scores based on Placebo Results-checkpoint.ipynb │ │ ├── Unaware of Regression Discontinuities-checkpoint.ipynb │ │ └── [WiP] Surrogate Models - What to Control For-checkpoint.ipynb │ ├── ATE vs ATT vs ATU.ipynb │ ├── Bad Control of Intermediate Outcomes.ipynb │ ├── Bias when Unconfoundedness is not Met.ipynb │ ├── Bootstrap vs Jackknife.ipynb │ ├── Clustering Standard Errors.ipynb │ ├── Inference on Winners.ipynb │ ├── Logarithm Transformations.ipynb │ ├── OutliersDemonstration.ipynb │ ├── PostEstimationPower.ipynb │ ├── Quantile Regression Example.ipynb │ ├── Trimming Propensity Scores based on Placebo Results.ipynb │ ├── Unaware of Regression Discontinuities.ipynb │ ├── [WiP] Surrogate Models - What to Control For.ipynb │ └── psm_example.ipynb ├── GradientDescent Example.ipynb ├── How Data Cleaning and Manipulation Affect Results │ ├── .ipynb_checkpoints │ │ ├── Data Aggregation and Bias-checkpoint.ipynb │ │ ├── DiD Role of Autocorrelation and Trend Size-checkpoint.ipynb │ │ └── Measurement Error Bias-checkpoint.ipynb │ ├── Data Aggregation and Bias.ipynb │ ├── DiD Role of Autocorrelation and Trend Size.ipynb │ └── Measurement Error Bias.ipynb ├── Identifying Variation for Estimates.ipynb ├── LaGrangian Optimization.ipynb ├── OLS via Gradient Descent and Matrix Inversion.ipynb ├── Price Demand Elasticity Example.ipynb ├── README.md └── Two Part Hurdle Model.ipynb ├── Presentations_Raw ├── .DS_Store ├── .ipynb_checkpoints │ ├── 1 Foundations 20230528 update-checkpoint.ipynb │ ├── 2 Causal Models 20230530 update-checkpoint.ipynb │ ├── 3 Inference 20230605 update-checkpoint.ipynb │ ├── 5 HTE Models 20230527 update-checkpoint.ipynb │ ├── 5 HTE Models 20230615 Update simulation part-checkpoint.ipynb │ ├── 5 HTE Models 20230615 update-checkpoint.ipynb │ └── 9 Arguable Validation 20230606 update-checkpoint.ipynb ├── 1 Foundations 20230528 update.ipynb ├── 1 Foundations 20230528 update.ipynb ├── 1 Foundations 20230528 update.slides.html ├── 2 Causal Models 20230530 update.ipynb ├── 3 Inference 20230605 update.ipynb ├── 3 Inference 2023jun05 update.slides.html ├── 5 HTE Models 20230527 update.ipynb ├── 5 HTE Models 20230527 update.slides.html ├── 5 HTE Models 20230615 Update simulation part.ipynb ├── 5 HTE Models 20230615 update.ipynb ├── 5 HTE Models 20230615 update.slides.html ├── 9 Arguable Validation 20230606 update.ipynb ├── 9 Arguable Validation 2023jun06 update.slides.html ├── Experimentation │ ├── .ipynb_checkpoints │ │ ├── 0 Why Experimentation-checkpoint.ipynb │ │ └── 99 Statistical Power-checkpoint.ipynb │ ├── 0 Why Experimentation.ipynb │ └── 99 Statistical Power.ipynb ├── Figures │ ├── ArguableValidation_Figure_1.png │ ├── ArguableValidation_Figure_2.png │ ├── ArguableValidation_Figure_3.png │ ├── ArguableValidation_Figure_4a.png │ ├── ArguableValidation_Figure_4b.png │ ├── Foundation_Figure1.png │ ├── Foundation_Figure2.png │ ├── HTE_Figure_0.png │ ├── HTE_Figure_1.png │ ├── HTE_Figure_10a.png │ ├── HTE_Figure_10x_friedman.png │ ├── HTE_Figure_10x_linear.png │ ├── HTE_Figure_10x_logit.png │ ├── 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