We present recent developments in double machine learning (DML) approach. The DML approach is concerned primarily with selecting the relevant control variables and functional forms necessary for the consistent estimation of an average treatment effect. We explain why the use of orthogonal moment conditions is crucial in this setting. We also discuss how DML approach can be applied to estimate the conditional average treatment effect (CATE) function conditional on a pre-specified coordinate.