Machine Learning: LASSO, Causality, and Prediction Concepts

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What is the main goal of machine learning tools according to the text?

Minimize MSE (correct)

Support causal inference

Reduce overfitting risk

Deliver reliable inference about specific parameters

Which challenge is NOT mentioned as a problem that Machine Learning can solve?

Model selection

Heteroscedasticity (correct)

Outliers/influential observations

Multicollinearity

What does overfitting refer to in the context of machine learning?

Including too many potential variables (correct)

Minimizing the residual sum of squares

Selecting the most robust functional forms

Including too few potential variables

Which method helps reduce the influence of outliers and select more robust functional forms?

Cross-validation Signup and view all the answers

In causal inference, what is intended to be delivered about specific parameters of a regression model?

Reliable inference Signup and view all the answers

What is another term used for regularized regression according to the text?

'Penalized regression' Signup and view all the answers

Which of the following is a key characteristic of statistical inference for the LASSO?

Rapidly evolving field Signup and view all the answers

What is a common misconception about hurdles like the IRC condition in statistical inference for the LASSO?

They are always significant and insurmountable Signup and view all the answers

Which approach is described as increasingly well-established for estimating treatment effects?

PDS approach Signup and view all the answers

What is a common theme regarding the new work in the field of statistical inference for the LASSO and related methods?

Continuously evolving research output Signup and view all the answers

Which publication provides insights into using machine learning methods to support causal inference in econometrics?

Ahrens et al. (2021) Signup and view all the answers

In which publication can one find information about high-dimensional methods and inference on structural and treatment effects?

"Review of Economic Studies" by Belloni, Chernozhukov, and Hansen (2014b) Signup and view all the answers

What is the purpose of Step 2 in the Post-Double-Selection (PDS) LASSO method?

Estimate the selected controls for the causal variable of interest. Signup and view all the answers

What is one advantage of the One Covariate at a Time Multiple Testing (OCMT) approach over LASSO for model selection?

It often performs as well as LASSO in selecting true variables. Signup and view all the answers

What is the purpose of estimating yi = β1xi,1 + β2xi,2 + ... + βpxi,p + ϵi in Step 1 of PDS LASSO?

To estimate the causal variable of interest without using di as a regressor. Signup and view all the answers

In machine learning, what does LASSO stand for?

Least Absolute Shrinkage and Selection Operator Signup and view all the answers

What is the key benefit of having non-selected regressors with a small correlation with the treatment and control in PDS LASSO?

They allow for asymptotically valid inference. Signup and view all the answers

Which approach does not rely on penalization for model selection?

OCMT approach Signup and view all the answers