Channel Title: Causal Deep Learning Channel Description: Deep Learning combined with Causal Inference. <video> 16.2) Matriarchal vs Patriarchal Society Description: </video> <video> 16.3) False Positives (Type I Error): Bonferroni Correction for Cherry Picking Description: G1) Null vs Alternative Hypothesis https://youtu.be/v4IO5XkH9QI​ G2) Type I vs Type II Errors https://youtu.be/Xbnrh8E8mXc 16.3) False Positives (Type I Error): Bonferroni Correction for Cherry Picking https://youtu.be/38jB4jLi0Rg </video> <video> 6.18) Book Review: Regression Analysis: A Practical Introduction Description: 6.1) Book Review: Mostly Harmless Econometrics https://youtu.be/iVCnm7okbD4 6.3) Book Review: Econometric Analysis of Cross Section and Panel Data https://youtu.be/M1C0kvtpkKw 6.6) Book Review: A Guide to Econometrics https://youtu.be/-dR8SgXnxQY 6.7) Book Review: Econometrics https://youtu.be/i6RWWltMc-Q 6.8) Introductory Books for Econometrics https://youtu.be/MbUHJKkEuUE 6.17) Book Review Essentials of Applied Econometrics https://youtu.be/qDN2cOjlvC0 6.18) Book Review: Regression Analysis: A Practical Introduction https://youtu.be/4JLQAGxAdmc 6.19) Book Review: Using Python for Introductory Econometrics https://youtu.be/V1lAQZj74SA </video> <video> 13.7) The Best Book to Learn Regression Discontinuity (RD) Description: 13.1) Sharp Regression Discontinuity https://youtu.be/_PQYKDrBU0w​ 13.2) Regression Discontinuity in Python https://youtu.be/FcFYYA8iMCs​ 13.3) Regression Discontinuity (RD) https://youtu.be/bgYSOtW6Www​ 13.4) Fuzzy Regression Discontinuity (FRD) https://youtu.be/eJKF2kAkh1Y​ 13.5) Fuzzy vs Sharp RD https://youtu.be/ec6J0AXikeQ​ 13.6) Python Fuzzy RD https://youtu.be/OpjhM5EhPhk 13.7) The Best Book to Learn Regression Discontinuity (RD) https://youtu.be/7oU4ipbht84 G5) Python: Binomial Test for Regression Discontinuity https://youtu.be/Y8xtq7qtKo0 </video> <video> 12.5) Introduction to Instrumental Variables (IV) Description: 12.1) Instrumental Variables (IV): Assumptions https://youtu.be/GT60wWNbLeA​ 12.2) Why Instrumental Variable? https://youtu.be/5U2YxKufepg​ 12.3) Two-Stage Least Squares (2SLS) https://youtu.be/qTX1lg8QKQ8​ 12.4) Python: IV and 2SLS https://youtu.be/DPFxUjCJE7k 12.5) Introduction to Instrumental Variables (IV) https://youtu.be/8UpEJltV2LU </video> <video> 14.5) Common Trend Assumption in Difference-in-Difference (DD) Estimator Description: 14.1) First-Difference Estimator https://youtu.be/p9NhSrTugYM​ 14.2) Algebra of Difference-in-Differences (DID) https://youtu.be/_mOJK8A6nR0​ 14.3) Python: Diff-in-Diff (DD) https://youtu.be/lxgGqV5zfnw​ 14.4) Quasi-Experiment Diff-in-Diff (DID) https://youtu.be/jDEe8qhpzRc 14.5) Common Trend Assumption in Difference-in-Difference (DD) Estimator https://youtu.be/B-Y4GpPYJ4Q </video> <video> 13.8) Fuzzy RD vs Naive OLS Description: 13.1) Sharp Regression Discontinuity https://youtu.be/_PQYKDrBU0w​ 13.2) Regression Discontinuity in Python https://youtu.be/FcFYYA8iMCs​ 13.3) Regression Discontinuity (RD) https://youtu.be/bgYSOtW6Www​ 13.4) Fuzzy Regression Discontinuity (FRD) https://youtu.be/eJKF2kAkh1Y​ 13.5) Fuzzy vs Sharp RD https://youtu.be/ec6J0AXikeQ​ 13.6) Python Fuzzy RD https://youtu.be/OpjhM5EhPhk​ 13.7) The Best Book to Learn Regression Discontinuity (RD) https://youtu.be/7oU4ipbht84 13.8) Fuzzy RD vs Naive OLS https://youtu.be/DPfv43z2Qo4 </video> <video> 15.8) OLS vs Fixed vs Random Effects Description: 15.1) Fixed Effects (FE): Time-Demeaned https://youtu.be/HYTdxssnZuI​ 15.2) Random Effects (RE) vs Fixed Effects (FE) https://youtu.be/L_RTB49Mmy8​ 15.3) Random Effects (RE) is Generalized Least Squares (GLS) https://youtu.be/0DXfmR5K1OA​ 15.4) Covariance Matrix: Random Effects (RE) https://youtu.be/XebzmI5XSuA​ 15.5) Random Effects as a Weighted Average of OLS and FE https://youtu.be/eM-fRB4cE-k​ 15.6) Python: Fixed and Random Effects https://youtu.be/Q0kossogTko 15.7) Time Trend vs Time Fixed Effects https://youtu.be/iUCKVC91o2U 15.8) OLS vs Fixed vs Random Effects https://youtu.be/EinYwMh4y5s </video> <video> 6.16) Stata vs Python Description: </video> <video> 15.7) Time Trend vs Time Fixed Effects Description: 15.1) Fixed Effects (FE): Time-Demeaned https://youtu.be/HYTdxssnZuI​ 15.2) Random Effects (RE) vs Fixed Effects (FE) https://youtu.be/L_RTB49Mmy8​ 15.3) Random Effects (RE) is Generalized Least Squares (GLS) https://youtu.be/0DXfmR5K1OA​ 15.4) Covariance Matrix: Random Effects (RE) https://youtu.be/XebzmI5XSuA​ 15.5) Random Effects as a Weighted Average of OLS and FE https://youtu.be/eM-fRB4cE-k​ 15.6) Python: Fixed and Random Effects https://youtu.be/Q0kossogTko 15.7) Time Trend vs Time Fixed Effects https://youtu.be/iUCKVC91o2U </video> <video> 6.20) What is the difference between an Economist, Econometrician, and Mathematician? Description: </video> <video> 16.1) Are Optical Illusions Universal? Description: </video> <video> 14.6) Introduction to Difference-in-Differences (DiD) Description: 14.1) First-Difference Estimator https://youtu.be/p9NhSrTugYM​​ 14.2) Algebra of Difference-in-Differences (DID) https://youtu.be/_mOJK8A6nR0​​ 14.3) Python: Diff-in-Diff (DD) https://youtu.be/lxgGqV5zfnw​​ 14.4) Quasi-Experiment Diff-in-Diff (DID) https://youtu.be/jDEe8qhpzRc​ 14.5) Common Trend Assumption in Difference-in-Difference (DD) Estimator https://youtu.be/B-Y4GpPYJ4Q 14.6) Introduction to Difference-in-Differences (DiD) https://youtu.be/qaSV2lx3K7c </video> <video> 6.17) Book Review Essentials of Applied Econometrics Description: 6.1) Book Review: Mostly Harmless Econometrics https://youtu.be/iVCnm7okbD4 6.3) Book Review: Econometric Analysis of Cross Section and Panel Data https://youtu.be/M1C0kvtpkKw 6.6) Book Review: A Guide to Econometrics https://youtu.be/-dR8SgXnxQY 6.7) Book Review: Econometrics https://youtu.be/i6RWWltMc-Q 6.8) Introductory Books for Econometrics https://youtu.be/MbUHJKkEuUE 6.17) Book Review Essentials of Applied Econometrics https://youtu.be/qDN2cOjlvC0 6.18) Book Review: Regression Analysis: A Practical Introduction https://youtu.be/4JLQAGxAdmc 6.19) Book Review: Using Python for Introductory Econometrics https://youtu.be/V1lAQZj74SA </video> <video> 6.19) Book Review: Using Python for Introductory Econometrics Description: 6.1) Book Review: Mostly Harmless Econometrics https://youtu.be/iVCnm7okbD4 6.3) Book Review: Econometric Analysis of Cross Section and Panel Data https://youtu.be/M1C0kvtpkKw 6.6) Book Review: A Guide to Econometrics https://youtu.be/-dR8SgXnxQY 6.7) Book Review: Econometrics https://youtu.be/i6RWWltMc-Q 6.8) Introductory Books for Econometrics https://youtu.be/MbUHJKkEuUE 6.17) Book Review Essentials of Applied Econometrics https://youtu.be/qDN2cOjlvC0 6.18) Book Review: Regression Analysis: A Practical Introduction https://youtu.be/4JLQAGxAdmc 6.19) Book Review: Using Python for Introductory Econometrics https://youtu.be/V1lAQZj74SA </video>

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Deep Learning combined with Causal Inference.

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