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Sofiia Dolgikh

Publications and source records attributed to Sofiia Dolgikh.

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mnorm: An R Package for Calculation and Differentiation of Conditional Multivariate Normal Densities and Probabilities

We introduce the mnorm package, which allows one to calculate conditional multivariate normal densities and probabilities and to differentiate them with respect to various parameters including covariances and integration limits. The package also supports parallel (multi-core) computing, handles non-normal marginals via the Gaussian copula, and provides fast routines for the calculation of bivariate and trivariate normal probabilities. The package is of special interest for the implementation of the maximum-likelihood estimators in econometric models.

econ.EM

Double machine learning for causal inference in a multivariate sample selection model

We propose plug-in (PI) and double machine learning (DML) estimators of average treatment effect (ATE), average treatment effect on the treated (ATET) and local average treatment effect (LATE) in the multivariate sample selection model with ordinal selection equations. Our DML estimators are doubly-robust and based on the efficient influence functions. Finite sample properties of the proposed estimators are studied and compared on simulated data. Specifically, the results of the analysis suggest that without addressing multivariate sample selection, the estimates of the causal parameters may be highly biased. However, the proposed estimators allow us to avoid these biases.

econ.EM