The characteristics of ML are basically (1) emphasis on overall modeling, for prediction (as opposed, for example, to emphasis on inference), (2) moreover, emphasis on non-causal modeling and prediction, (3) emphasis on computationally-intensive methods and algorithmic development, and (4) emphasis on large and often high-dimensional datasets.
Readers of this blog will recognize the ML characteristics as closely matching those of TSE! Rob Engle's V-Lab at NYU Stern's Volatility Institute, for example, embeds all of (1)-(4). So TSE and ML have a lot to learn from each other, but the required bridge is arguably quite short.
Interestingly, Athey and Imbens come not from the TSE tradition, but rather from the CSE tradition, which typically emphasizes causal estimation and inference. That makes for a longer required CSE-ML bridge, but it may also make for a larger payoff from building and crossing it (in both directions).
In any event I share Athey and Imbens' excitement, and I welcome any and all cross-fertilization of ML, TSE and CSE.