Inference on treatment effects after selection among high-dimensional controls

Controls selection after

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Inference on Treatment Effects After Selection Amongst High-Dimensional Controls. , Victor Chernozhukov, et al. Hansen Stata and Matlab programs are available here; 54. The papers “Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain” with A.

Chernozhukov and C. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances. Chernozhukov (Review of Economic Studies, ) present. An application is e. We allow for the inference on treatment effects after selection among high-dimensional controls number of regressors to be larger than the sample size. However, there has been little work on inference after imperfect model selection.

We propose robust methods for inference on the effect of a treatment variable on a scalar inference on treatment effects after selection among high-dimensional controls outcome in the presence of very many controls. Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian, Inference on Treatment Effects after Selection Amongst High-Dimensional Controls (). What I understand is that they assume that the outcome can be well approximated by a small/sparse set of controls but the researcher does not know which those controls are from a high dimensional set of possible controls.

inference on treatment effects after selection among high-dimensional controls American Economic Review 105: 486–90. In the presence of high dimensional controls, Belloni inference on treatment effects after selection among high-dimensional controls et al. (See also Belloni, Chernozhukov, Fernandéz-Val, and Hansen. Thus our method resolves the problem of uniform inference after model selection for a large, interesting class of models. Valid post-selection and post-regularization inference: An elementary, general approach. CrossRef Google Scholar. Provides valid confidence intervals after a selection of many instruments. The Review of Economic Studies 81 608–650.

CiteSeerX — INFERENCE ON TREATMENT EFFECTS AFTER SELECTION AMONGST HIGH-DIMENSIONAL CONTROLS. We develop a novel estimation and uniformly valid inference method for. Belloni, inference on treatment effects after selection among high-dimensional controls inference on treatment effects after selection among high-dimensional controls Alexandre, Victor inference on treatment effects after selection among high-dimensional controls Chernozhukov, and Christian Hansen. " The Review of Economic Studies 81. Inference on Treatment inference on treatment effects after selection among high-dimensional controls Effects After Selection Amongst High-Dimensional Controls Item Preview remove-circle Share or Embed This Item. MIT Department of Economics Working Paper inference on treatment effects after selection among high-dimensional controls No.

,, pp. Inference on treatment effects after selection amongst high-dimensional controls. the variable selection methods choose a di erent set of variables than those that have inference on treatment effects after selection among high-dimensional controls been employed and that the estimated treatment e ect of abortion inference on treatment effects after selection among high-dimensional controls on crime is inference on treatment effects after selection among high-dimensional controls very imprecise after controlling for the selected variables. , Chernozhukov V. (, ) proposed a post double selection procedure for estimation and inference; Lee et al.

Our setting is a partially linear model. BibTeX author = A. We propose robust methods for inference on the effect of a treatment variable on a scalar outcome inference on treatment effects after selection among high-dimensional controls in the presence of very many controls. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls. inference on treatment effects after selection among high-dimensional controls : 608–50. Main idea: Select instrument via the Lasso. "Inference on treatment effects after selection among high-dimensional controls. 29-50 Google Scholar.

In observational studies, estimation of a causal effect inference on treatment effects after selection among high-dimensional controls of a treatment on an outcome relies on proper adjustment for confounding. We propose robust methods for inference about the eect of a treatment variable on a scalar outcome in the presence of inference on treatment effects after selection among high-dimensional controls very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. This paper addresses the theoretical properties of an OLS estimate of the effect of a variable after selecting the "other" controls using LASSO.

Hansen () and Belloni et al. Electronic copy available at: com/abstract=129 Massachusetts Institute of Technology Department of Economics Working Paper Series INFERENCE ON. High-Dimensional inference on treatment effects after selection among high-dimensional controls Methods and Inference on Structural and Treatment Effects 5 The penalty level, λ, controls the degree of penalization. There has been extensive work on estimation and perfect model selection in both low and high-dimensional contexts; see, e.

"Inference on Treatment Effects After Selection Amongst High-Dimensional Controls (with an Application to Abortion and Crime),", The Review of Economic Studies, with A. The Review of Economic Studies 81 (2), 608 – 650. Inference on Treatment Effects after Selection among High-Dimensional Controls† Belloni, Alexandre; Chernozhukov, inference on treatment effects after selection among high-dimensional controls Victor; Hansen, Christian:00:00 We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. Robust inference on average treatment. Working Paper: Inference on Treatment Effects After Selection inference on treatment effects after selection among high-dimensional controls Amongst High-Dimensional Controls () Working Paper: Inference on treatment effects after selection amongst high-dimensional controls () This item may be available elsewhere in EconPapers: Search for items with the same title. The functions estimates (low-dimensional) target coefficients in a high-dimensional linear model. There has been extensive work on estimation and perfect model selection in both low and high-dimensional contexts; see, e.

INFERENCE ON TREATMENT EFFECTS AFTER SELECTION AMONGST HIGH-DIMENSIONAL CONTROLS A. Key Words: treatment e ects, high-dimensional-sparse regression, robust inference under imperfect model selection. Inference on treatment effects after selection among high-dimensional controls. () Inference on treatment effects after selection among high-dimensional controls. "Inference on Treatment Effects after Selection among High-dimensional inference on treatment effects after selection among high-dimensional controls Controls. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances where the number of controls may be much larger than the sample size. Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls". " The Review of Economic Studies 81, no.

Our setting is a partially linear model with. If the number of the potential confounders (p) is larger than the number of observations (n), then direct control for all potential confounders is infeasible. inference on treatment effects after selection among high-dimensional controls By Alexandre Belloni, Victor Chernozhukov and Christian Hansen. We propose robust methods for inference on the effect of a trea tment variable on a scalar outcome in the presence of very many controls.

The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of controls. We propose robust methods for inference on the e ect of a treatment variable on a scalar outcome in the presence of very many controls. () for reviews inference on treatment effects after selection among high-dimensional controls focused on econometric applications. I am reading the work by Belloni et al (), see the name in the title (weblink here). Annual Review of Economics 7: 649–688. Practical choices for λ that provably guard against overfitting are provided in Belloni, Chen, Chernozkukov, and Hansen (). Chernzhukov (Econometrica, ) and “Inference on Treatment Effects after Selection amongst High-Dimensional Controls” with A. The performance of most of these methods depends.

, & Hansen, C. Hansen, title = INFERENCE ON TREATMENT EFFECTS AFTER SELECTION AMONGST HIGH-DIMENSIONAL CONTROLS: FURTHER DISCUSSION OF EMPIRICAL EXAMPLE, year =. Inference on treatment effects after selection from among high-dimensional controls We consider the case where a researcher is interested inference on treatment effects after selection among high-dimensional controls in estimating the treatment effect α 0 of a treatment variable d i. uniformly valid inference method for the treatment inference on treatment effects after selection among high-dimensional controls effect in this setting, called the "post-double-selection" method. , Chernozhukov, V. characterized the distribution of a post-LASSO-selection estimator conditioned on the selected variables, but only for linear regressions. Proposed Approach I: Inference with Selection among Many Instruments y i = d i + i (2) d i= z0 + r i + v i (3) d i is a scalar endogenous variable of interest.

The knockoff filter for fdr control in group-sparse and multitask regression. The main attractive feature of our method is that it allows for imperfect selection of the controls and provides confidence intervals that are valid uniformly across a large class of models. CHERNOZHUKOV, inference on treatment effects after selection among high-dimensional controls AND C. Post-selection and post-regularization inference in linear models with inference on treatment effects after selection among high-dimensional controls many controls and instruments. To make informative inference feasible, we require the model to be approximately sparse. estimation of a treatment effect α_0 in a setting of high-dimensional controls. In contrast, standard post-model selection estimators inference on treatment effects after selection among high-dimensional controls fail to provide uniform inference even in simple cases with a small, fixed number of controls.

The user can choose between the so-called post-double-selection method and partialling-out. z i is a p-dimensional vector of instruments Where the number of instruments p may be much larger than the number of observations. has a high-dimensional set of potential control variables, and needs to strike a. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small. High-dimensional methods and inference on structural and treatment effects J.

Inference on treatment effects after selection among high-dimensional controls

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