Instrumental variables (IVs) are a commonly used technique for causal inference from observational data. However, in the recent years, the use of the IV method has come under much criticism across multiple disciplines (e.g. [1] and [6] in Economics; [3] in Marketing; [5], [4], and [2] in Finance). This is because, in practice, the variation induced by IVs can be limited, which yields imprecise or biased estimates of causal effects and renders the approach ineffective for policy decisions. In this paper, we confront these challenges by formulating the problem of constructing instrumental variables from candidate exogenous information as a (supervised) machine learning problem that is amenable to the learning approach. We extend the standard learning framework to develop an algorithm we term MLIV (machine-learned instrumental variables), which allows training of instruments and causal inference to be simultaneously performed from sample data. We provide formal asymptotic theory and show O(√n) consistency and asymptotic normality for machine-learned instrument variables. We illustrate the effectiveness of the MLIVs in empirical environments consisting of both linear and nonlinear model parameters. Simulations and application to real-world data demonstrate that the algorithm is highly effective and significantly improves the performance of causal inference from observational data. The complete version of this paper can be found at https://ssrn.com/abstract=3352957.