max tokens
├── .github
├── FUNDING.yml
├── ISSUE_TEMPLATE
│ └── chapter-xxx.md
└── PULL_REQUEST_TEMPLATE
│ └── pull_request_template.md
├── .gitignore
├── LICENSE
├── README.md
├── Simulated-Data.ipynb
└── causal-inference-for-the-brave-and-true
├── 01-Introduction-To-Causality.ipynb
├── 02-Randomised-Experiments.ipynb
├── 03-Stats-Review-The-Most-Dangerous-Equation.ipynb
├── 04-Graphical-Causal-Models.ipynb
├── 05-The-Unreasonable-Effectiveness-of-Linear-Regression.ipynb
├── 06-Grouped-and-Dummy-Regression.ipynb
├── 07-Beyond-Confounders.ipynb
├── 08-Instrumental-Variables.ipynb
├── 09-Non-Compliance-and-LATE.ipynb
├── 10-Matching.ipynb
├── 11-Propensity-Score.ipynb
├── 12-Doubly-Robust-Estimation.ipynb
├── 13-Difference-in-Differences.ipynb
├── 14-Panel-Data-and-Fixed-Effects.ipynb
├── 15-Synthetic-Control.ipynb
├── 16-Regression-Discontinuity-Design.ipynb
├── 17-Predictive-Models-101.ipynb
├── 18-Heterogeneous-Treatment-Effects-and-Personalization.ipynb
├── 19-Evaluating-Causal-Models.ipynb
├── 20-Plug-and-Play-Estimators.ipynb
├── 21-Meta-Learners.ipynb
├── 22-Debiased-Orthogonal-Machine-Learning.ipynb
├── 23-Challenges-with-Effect-Heterogeneity-and-Nonlinearity.ipynb
├── 24-The-Diff-in-Diff-Saga.ipynb
├── 25-Synthetic-Diff-in-Diff.ipynb
├── Conformal-Inference-for-Synthetic-Control.ipynb
├── Debiasing-with-Orthogonalization.ipynb
├── Debiasing-with-Propensity-Score.ipynb
├── Prediction-Metrics-For-Causal-Models.ipynb
├── When-Prediction-Fails.ipynb
├── _config.yml
├── _toc.yml
├── data
├── ak91.csv
├── app_engagement_push.csv
├── billboard_impact.csv
├── collections_email.csv
├── customer_features.csv
├── customer_transactions.csv
├── drinking.csv
├── enem_scores.csv
├── hospital_treatment.csv
├── ice_cream_sales.csv
├── ice_cream_sales_rnd.csv
├── img
│ ├── beyond-conf
│ │ ├── cop-ex1.png
│ │ ├── cop-ex2.png
│ │ ├── cop.png
│ │ ├── selection.png
│ │ ├── shocked.png
│ │ └── y-pred.png
│ ├── brave-and-true.png
│ ├── causal-graph
│ │ ├── both_crap.png
│ │ ├── selection_bias.png
│ │ └── three_bias.png
│ ├── causal-model
│ │ ├── customers.png
│ │ ├── economists.png
│ │ ├── elast-partition.png
│ │ ├── elast-split.png
│ │ ├── elasticity.png
│ │ ├── linear-fix.png
│ │ ├── y-partition.png
│ │ └── y-split.png
│ ├── curse-of-dimensionality.jpg
│ ├── debias-ps
│ │ ├── again.png
│ │ ├── fear-no-man.png
│ │ └── ps-experiment.png
│ ├── debiased-ml
│ │ ├── avengers.png
│ │ ├── cf-pred.png
│ │ ├── cross-prediction.png
│ │ ├── diagram.png
│ │ ├── drown-ols.png
│ │ ├── linear-aprox.png
│ │ ├── ml-problem.png
│ │ ├── more-metrics.png
│ │ ├── non-linear.png
│ │ └── non-sci.png
│ ├── did-saga
│ │ ├── awful.jpeg
│ │ ├── death.png
│ │ ├── promise.png
│ │ └── twfeworking.png
│ ├── diff-in-diff
│ │ ├── cant-read.png
│ │ ├── non-parallel.png
│ │ └── secrets.png
│ ├── doubly-robust
│ │ └── double.png
│ ├── dummy
│ │ ├── heterosk.png
│ │ └── you_little_shit.png
│ ├── evaluate-causal-models
│ │ ├── ate_bins.png
│ │ ├── cumm_elast.png
│ │ ├── cumm_gain.png
│ │ ├── sneak.png
│ │ └── uncivilised.png
│ ├── fixed-effects
│ │ ├── control-unit.png
│ │ ├── demeaned.png
│ │ ├── fe-graph.png
│ │ └── time-travel.png
│ ├── graph-flow.png
│ ├── hte-binary-outcome
│ │ ├── logistic.png
│ │ └── slide-susan-athey.png
│ ├── industry-ml
│ │ ├── cascade-1.png
│ │ ├── cascade-2.png
│ │ ├── cashflow-1.png
│ │ ├── cross-validation.png
│ │ ├── ml-flow.png
│ │ ├── ml-meme.png
│ │ ├── model-fit.png
│ │ ├── overfitting.jpg
│ │ ├── profit.png
│ │ ├── test.png
│ │ └── translation.png
│ ├── intro
│ │ ├── anatomy1.png
│ │ ├── anatomy2.png
│ │ ├── anatomy3.png
│ │ ├── anatomy4.png
│ │ ├── potential_outcomes.png
│ │ └── tougher-up-cupcake1.jpg
│ ├── iv
│ │ ├── good-iv.png
│ │ ├── incomplete-files.png
│ │ ├── qob.png
│ │ └── weak-iv.png
│ ├── late
│ │ ├── always_takers.png
│ │ ├── defiers.png
│ │ └── double_index.png
│ ├── linear-regression
│ │ ├── confused_cat.png
│ │ └── you_vs.png
│ ├── matching
│ │ ├── explain.png
│ │ ├── its-a-match.png
│ │ └── ubiquitous-ols.png
│ ├── meta-learners
│ │ ├── learned-new-move.png
│ │ ├── not-great-not-terrible.jpeg
│ │ ├── s-learner.png
│ │ ├── second-stage-x.png
│ │ ├── t-learner-problem.png
│ │ ├── t-learner.png
│ │ ├── t-vs-x-learner.png
│ │ ├── x-learner.png
│ │ └── zero-bias-s-learner.png
│ ├── orthogonal
│ │ ├── athlas.png
│ │ ├── kfold-cv.png
│ │ └── nazare-confusa.jpg
│ ├── plug-and-play-estimators
│ │ ├── genious.jpeg
│ │ ├── infeasible.png
│ │ ├── linear-case.png
│ │ ├── non-linear-case.png
│ │ └── second-estimator.png
│ ├── poetry.png
│ ├── ps
│ │ ├── balance.png
│ │ ├── bootstrap.png
│ │ ├── iptw.png
│ │ └── ml-trap.png
│ ├── rct
│ │ ├── indep.png
│ │ ├── lurking_bias.png
│ │ └── science_dream.png
│ ├── rdd
│ │ ├── ols.png
│ │ ├── rdd.png
│ │ ├── rdd_fuzzy.png
│ │ └── smooth.png
│ ├── sc-conformal-inf
│ │ ├── aug-data.png
│ │ └── block-perm.png
│ ├── sdid
│ │ └── both-pills.png
│ ├── stats-review
│ │ ├── ci_xkcd.png
│ │ ├── normal_dist.jpeg
│ │ └── p_value.png
│ ├── synth-control
│ │ ├── allways.png
│ │ ├── extrapolation.png
│ │ ├── out-of-sample.png
│ │ ├── regr_space.png
│ │ ├── regr_space_x.png
│ │ ├── regr_time.png
│ │ └── synth_img.png
│ └── when-prediction-fails
│ │ ├── flat-curves.png
│ │ ├── graph-1.png
│ │ ├── graph-2.png
│ │ ├── graph-3.png
│ │ ├── horse-meme.png
│ │ ├── ml-in-5.png
│ │ ├── model-opt.png
│ │ ├── opt-deriv.png
│ │ ├── pandas-magic.png
│ │ ├── partitions.png
│ │ ├── personalise.png
│ │ ├── slice-1.png
│ │ ├── slice-2.png
│ │ └── waiting-time.png
├── invest_email.csv
├── invest_email_biased.csv
├── invest_email_rnd.csv
├── learning_mindset.csv
├── medicine_impact_recovery.csv
├── online_classroom.csv
├── sheepskin.csv
├── smoking.csv
├── trainees.csv
├── wage.csv
└── when-prediction-fails
│ ├── graph-1.png
│ ├── graph-2.png
│ ├── graph-3.png
│ ├── horse-meme.png
│ ├── ml-in-5.png
│ ├── model-opt.png
│ ├── opt-deriv.png
│ ├── pandas-magic.png
│ ├── partitions.png
│ ├── personalise.png
│ ├── slice-1.png
│ ├── slice-2.png
│ └── waiting-time.png
├── landing-page.md
├── nb18.py
├── nb21.py
└── sc.py
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1 | ## Status
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3 |
4 |
5 | ## Description
6 |
7 |
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