000			02981cam a22003857 4500
001			w26584
003			NBER
005			20211020103920.0
006			m o d
007			cr cnu||||||||
008			210910s2019 mau fo 000 0 eng d
100	1		_aAngrist, Joshua. _932861
245	1	0	_aMachine Labor / _cJoshua Angrist, Brigham Frandsen.
260			_aCambridge, Mass. _bNational Bureau of Economic Research _c2019.
300			_a1 online resource: _billustrations (black and white);
490	1		_aNBER working paper series _vno. w26584
500			_aDecember 2019.
520	3		_aMachine learning (ML) is mostly a predictive enterprise, while the questions of interest to labor economists are mostly causal. In pursuit of causal effects, however, ML may be useful for automated selection of ordinary least squares (OLS) control variables. We illustrate the utility of ML for regression-based causal inference by using lasso to select control variables for estimates of effects of college characteristics on wages. ML also seems relevant for an instrumental variables (IV) first stage, since the bias of two-stage least squares can be said to be due to over-fitting. Our investigation shows, however, that while ML-based instrument selection can improve on conventional 2SLS estimates, split-sample IV, jackknife IV, and LIML estimators do better. In some scenarios, the performance of ML-augmented IV estimators is degraded by pretest bias. In others, nonlinear ML for covariate control creates artificial exclusion restrictions that generate spurious findings. ML does better at choosing control variables for models identified by conditional independence assumptions than at choosing instrumental variables for models identified by exclusion restrictions.
530			_aHardcopy version available to institutional subscribers
538			_aSystem requirements: Adobe [Acrobat] Reader required for PDF files.
538			_aMode of access: World Wide Web.
588	0		_aPrint version record
690		7	_aC21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions _2Journal of Economic Literature class.
690		7	_aC26 - Instrumental Variables (IV) Estimation _2Journal of Economic Literature class.
690		7	_aC52 - Model Evaluation, Validation, and Selection _2Journal of Economic Literature class.
690		7	_aC55 - Large Data Sets: Modeling and Analysis _2Journal of Economic Literature class.
690		7	_aJ01 - Labor Economics: General _2Journal of Economic Literature class.
690		7	_aJ08 - Labor Economics Policies _2Journal of Economic Literature class.
700	1		_aFrandsen, Brigham.
710	2		_aNational Bureau of Economic Research.
830		0	_aWorking Paper Series (National Bureau of Economic Research) _vno. w26584.
856	4	0	_uhttps://www.nber.org/papers/w26584
856			_yAcceso en línea al DOI _uhttp://dx.doi.org/10.3386/w26584
942			_2ddc _cW-PAPER
999			_c321538 _d280100