Relevant webpages for Python-R-Machine Learning

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• http://web.pdx.edu/~crkl/

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tex analytics, ML, economics in Brown - https://sites.google.com/view/amy-handlan/teaching#h.p7h7tdhmgjsd

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Pearls causal epistemology - https://twitter.com/soboleffspaces/status/1490513077019627520/photo/1

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how to code in Python - https://www.python.org/dev/peps/pep-0008/#package-and-module-names

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Causal Inference and Data Fusion in Econometrics - https://arxiv.org/pdf/1912.09104.pdf

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Causal inference in statistics: An overview - https://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf

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DAGs for teaching - https://twitter.com/AsjadNaqvi/status/1492266270921838604

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Gift regressions - https://sites.google.com/view/robertostling/home/teaching

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Parallel computing using python - https://twitter.com/radekosmulski/status/1495515618770096130/photo/1

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Introduction to econometrics in R - https://github.com/NickCH-K/Econometrics

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causality and econometrics - https://www.nber.org/papers/w29787

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causal dags - https://jamanetwork.com/journals/jama/fullarticle/2789646?utm_source=twitter&utm_campaign=content-shareicons&utm_content=article_engagement&utm_medium=social&utm_term=022822#.Yh04zhCuXkI.twitter

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causality UCLA - https://www.youtube.com/watch?v=ci3muJMEYho

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RCT example Pearl - https://ftp.cs.ucla.edu/pub/stat_ser/r513.pdf

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Athey and Imbens on ML tools - https://www.annualreviews.org/doi/pdf/10.1146/annurev-economics-080217-053433

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data Visualization - https://twitter.com/KirkDBorne/status/1500180801652248576

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introduction to probabilities - https://github.com/norvig/pytudes/blob/main/ipynb/Probability.ipynb

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Pearl vs Imbens http://causality.cs.ucla.edu/blog/index.php/2014/11/29/on-the-first-law-of-causal-inference/

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High Performance Computing with Julia for macro - https://colab.research.google.com/drive/1Dt_Ah1X1e93PYWgxtjLtPg0nQRvC5rBR

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Edward Kennedy DML-https://twitter.com/edwardhkennedy/status/1045652861605097476?s=20&t=V6zuHvN7gvb_DMwGFAjIOQ

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Econometrics with python - https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2021.pdf

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DML - http://aeturrell.com//2018/02/10/econometrics-in-python-partI-ML/ https://www.r-bloggers.com/2017/06/cross-fitting-double-machine-learning-estimator/

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https://www.causalscience.org/blog/how-to-push-causal-inference-in-industry/

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color equations latex - https://github.com/synercys/annotated_latex_equations/blob/main/eqn_annotate.tex

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statistical tools for causal inference - https://chabefer.github.io/STCI/

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Deep learning visualization - https://towardsdatascience.com/deep-learning-with-python-neural-networks-complete-tutorial-6b53c0b06af0

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jobs for julia experts - https://towardsdatascience.com/how-to-get-a-job-programming-in-julia-7f24a2ee563f

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DAGS - https://www.causalscience.org/blog/introduction-to-dags-their-applicability-and-how-they-come-about/

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Computer science and causal inference conference - https://why21.causalai.net/

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https://twitter.com/soboleffspaces/status/1518643924180422656/photo/1

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http://book.d2l.ai/index.html

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Design and Analysis of Experiments and Observational Studies using R - http://designexptr.org/causal-inference.html

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CATE meets ML - https://github.com/QuantLet/Meta_learner-for-Causal-ML

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Angrist and Imbens story about the nobel prize - https://www.gsb.stanford.edu/insights/unexpected-result-how-nobelist-guido-imbens-helped-kick-start-credibility-revolution

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Athey's Matrix Completion Methods https://sidravi1.github.io/blog/2018/12/02/athey-s-matrix-completion-methods

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Program Evaluation - https://ds4ps.org/PROG-EVAL-III/index.html

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selection bias - https://twitter.com/xkcd/status/1524497524391825409/photo/1

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On Pearl’s Hierarchy and the Foundations of Causal Inference - https://causalai.net/r60.pdf

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Causal Inference With Python - https://analyticsindiamag.com/a-complete-guide-to-causal-inference-in-python/

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Introduction to computational thinking JULIA MIT - https://computationalthinking.mit.edu/Spring21/

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CAUSLA FOREST RDD - https://arxiv.org/pdf/2106.11640.pdf

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https://twitter.com/llopatin/status/1540800782198382593/photo/1

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https://pythontutor.com/ Tool to visualize Class objects in Python.

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https://docs.google.com/document/d/1jBGk-u5auVCvI5EMaQnptMWbxHHl3ncIhKgIgEzjylw/edit

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https://harvard-iacs.github.io/2020-CS109A/

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https://hai.stanford.edu/news/technology-economist-susan-athey-adds-doj-role-her-multidimensional-career?utm_source=twitter&utm_medium=social&utm_content=Stanford%20HAI_twitter_StanfordHAI_202207070934_sf167251934&utm_campaign=&sf167251934=1

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https://twitter.com/amt_shrma/status/1553038161386676225, ICML & Causal Inference!