| 1 - A large scale job counseling experiment.mp4 | 28.5 MB | ||
| 1 - Experiment - Study the Impact of Search Engine Advertisement.mp4 | 31.7 MB | ||
| 1 - Linear regression.mp4 | 44.5 MB | ||
| 1 - Machine Learning.mp4 | 27.2 MB | ||
| 1 - Overview.mp4 | 43.9 MB | ||
| 10 - Bagging in Random Forests.mp4 | 20.9 MB | ||
| 10 - Example continued..(1).mp4 | 6.4 MB | ||
| 10 - IV regression in our job counseling application.mp4 | 26.3 MB | ||
| 10 - R-squared.mp4 | 11 MB | ||
| 10 - The parallel trends assumption.mp4 | 43 MB | ||
| 11 - Example continued..(2).mp4 | 21.9 MB | ||
| 11 - Excursion Should we worry about the small R-squared in the regressions.mp4 | 41.7 MB | ||
| 11 - Hyperparameter Tuning.mp4 | 27.1 MB | ||
| 11 - Wine out-of-sample prediction.mp4 | 30.5 MB | ||
| 12 - Example continued..(3).mp4 | 31.9 MB | ||
| 12 - Hyperparameter tuning with a validation data set.mp4 | 29.8 MB | ||
| 12 - Which average treatment effect does our IV estimator estimate.mp4 | 32.2 MB | ||
| 13 - Indogeneity and Exogeneity.mp4 | 55.6 MB | ||
| 13 - The Rubin causal model and average treatment effects.mp4 | 87.4 MB | ||
| 13 - k-fold cross validation.mp4 | 31.8 MB | ||
| 14 - Causal graphs and endogeneity.mp4 | 39.5 MB | ||
| 14 - IV estimator estimates local average treatment effect (LATE).mp4 | 83.1 MB | ||
| 15 - Bias of OLS estimator in the homework example.mp4 | 18.1 MB | ||
| 15 - Not perfectly randomized treatment allocation.mp4 | 16.8 MB | ||
| 16 - Bias of OLS estimator in the homework example cont..(1).mp4 | 17.5 MB | ||
| 16 - Region fixed effects or inverse probability weighting.mp4 | 47.9 MB | ||
| 17 - An example to motivate inverse probability weighting.mp4 | 120.3 MB | ||
| 17 - Bias of OLS estimator in the homework example cont...(2).mp4 | 37.3 MB | ||
| 18 - 4 regressions for our inverse probability weighting example and discussion.mp4 | 84.6 MB | ||
| 18 - Bias of OLS estimator in the homework example cont...(3).mp4 | 15.9 MB | ||
| 19 - Back to our job-counseling application.mp4 | 44.4 MB | ||
| 19 - Bias of OLS estimator in the homework example cont...(4).mp4 | 13.8 MB | ||
| 2 - Controlled Experiments to Study Revenue Impact of SEM.mp4 | 38.6 MB | ||
| 2 - Estimating sample linear regression example.mp4 | 28.3 MB | ||
| 2 - Experiment continued - the OLS Regression.mp4 | 42.5 MB | ||
| 2 - Linear Regression.mp4 | 30.2 MB | ||
| 2 - Predications for training data sets.mp4 | 10.3 MB | ||
| 20 - Bias of OLS estimator in the homework example cont...(5).mp4 | 16.6 MB | ||
| 21 - Bias of OLS estimator in the homework example cont...(6).mp4 | 20.5 MB | ||
| 22 - Are there sources of exogenous variation in our homework example.mp4 | 32.6 MB | ||
| 23 - The causal effects of education.mp4 | 13.5 MB | ||
| 24 - Abstract of two empirical studies.mp4 | 116.4 MB | ||
| 25 - Randomized Experiments.mp4 | 29.5 MB | ||
| 26 - Interpreting Effect Sizes.mp4 | 30.5 MB | ||
| 27 - Non-linear effects.mp4 | 67.8 MB | ||
| 28 - Heterogeneous effects.mp4 | 46.8 MB | ||
| 3 - Estimating sample linear regression continued...(1).mp4 | 52.7 MB | ||
| 3 - Experiment continued - the endogeneity problem.mp4 | 22.5 MB | ||
| 3 - Experiment continued.mp4 | 19.4 MB | ||
| 3 - Ordinary Least Squares Estimation.mp4 | 14.5 MB | ||
| 3 - Predications for test data sets.mp4 | 9.4 MB | ||
| 4 - Difference in Difference Estimator.mp4 | 55.1 MB | ||
| 4 - Estimating sample linear regression continued...(2).mp4 | 15 MB | ||
| 4 - Estimating the Linear Regression in R.mp4 | 36.6 MB | ||
| 4 - Instrumental Variable Estimation.mp4 | 24.1 MB | ||
| 4 - Mean absolute error and root mean squared error.mp4 | 20.8 MB | ||
| 5 - DiD estimation via linear regression.mp4 | 70 MB | ||
| 5 - Estimating sample linear regression continued...(3).mp4 | 30.8 MB | ||
| 5 - Instrumental Variable Estimation in a Simple Linear Regression.mp4 | 13.3 MB | ||
| 5 - Overfitting.mp4 | 24.9 MB | ||
| 5 - Regression Results and Adding Age as Explanatory Variable.mp4 | 21.6 MB | ||
| 6 - DiD estimation via linear regression cont.mp4 | 14.9 MB | ||
| 6 - Explaining effect of age on temperature coefficient.mp4 | 43.7 MB | ||
| 6 - Formula standard error in simple linear regression.mp4 | 23.8 MB | ||
| 6 - IV Estimation via Two-Stage Least Squares.mp4 | 45.2 MB | ||
| 6 - Regression Trees.mp4 | 17.8 MB | ||
| 7 - Channel Variables.mp4 | 20.5 MB | ||
| 7 - Direct IV estimation.mp4 | 19.6 MB | ||
| 7 - Estimating a regression tree.mp4 | 25.7 MB | ||
| 7 - Fixed Effects Estimator.mp4 | 72.9 MB | ||
| 7 - Simulated emperical distribution, convergence of OLS estimator, coefficients.mp4 | 29.7 MB | ||
| 8 - Cluster-Robust Standard Errors.mp4 | 43.9 MB | ||
| 8 - Confidence intervals, t-values, p-values and significance stars.mp4 | 67.5 MB | ||
| 8 - IV estimation with more than one instrument and with control variables.mp4 | 62.9 MB | ||
| 8 - Optional Regression trees and dummy variable regression.mp4 | 13.8 MB | ||
| 8 - Unobserved confounders.mp4 | 47 MB | ||
| 9 - Discussion The Effects of Search Engine Marketing.mp4 | 74 MB | ||
| 9 - Example homework.mp4 | 11.5 MB | ||
| 9 - IV estimation in our job counseling application.mp4 | 22 MB | ||
| 9 - Log-level regression.mp4 | 48.4 MB | ||
| 9 - Random forests.mp4 | 8 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 83 total files | |||
Applied Empirical Economics with R and Machine Learning
https://WebToolTip.com
Published 11/2025
Created by Science Academy,Andrew Misseldine
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 81 Lectures ( 6h 19m ) | Size: 2.81 GB
Experiments, Regression & Causal Analysis for Predictive Modeling and Policy Evaluation
What you'll learn
Fundamentals of linear regression and ordinary least squares (OLS) estimation
How to implement regression models in R
Techniques to handle confounding variables and unobserved heterogeneity
Predictive modeling using machine learning: regression trees, random forests, and cross-validation
Deep dive into causal inference: endogeneity, instrumental variables, and treatment effects
Design and analysis of controlled experiments and difference-in-differences (DiD)
Application of instrumental variable estimation and inverse probability weighting
Real-world case studies including job counseling experiments and search engine marketing
Requirements
Basic understanding of statistics and data analysis
Familiarity with R is helpful
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