Generic machine learning inference on heterogenous treatment effects in randomized experiments

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. The approach is valid in high dimensional settings, where the effects are proxied by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. Our approach is agnostic and does not make unrealistic or hard-to-check assumptions; we don’t require conditions for consistency of the ML methods. Estimation and inference relies on repeated data splitting to avoid overfitting and achieve validity. For inference, we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity. This variational inference method is shown to be uniformly valid and quantifies the uncertainty coming from both parameter estimation and data splitting. The inference method could be of substantial independent interest in many machine learning applications. An empirical application to the impact of micro-credit on economic development illustrates the use of the approach in randomized experiments. An additional application to the impact of the gender discrimination on wages illustrates the potential use of the approach in observational studies, where machine learning methods can be used to condition flexibly on very high-dimensional controls.https://arxiv.org/abs/1712.04802First author draf

Can we measure hospital quality from physicians' choices?

In this paper, we propose an alternative methodology for ranking hospitals based on the choices of Medical School graduates over hospital training vacancies. Our methodology is therefore a revealed preference approach. Our methodology for measuring relative hospital quality has the following desirable properties: a) robust to manipulation from hospital administrators; b) conditional on having enough observations, it allows for differences in quality across specialties within a hospital; c) inexpensive in terms of data requirements, d) not subject to selection bias from patients nor hospital screening of patients; and e) unlike other rankings based on experts' evaluations, it does not require physicians to provide a complete ranking of all hospitals. We apply our methodology to the Spanish case and find, among other results, the following: First, the probability of choosing the best hospital relative to the worst hospital is statistically significantly different from zero. Second, physicians value proximity and nearby hospitals are seen as more substitutable. Third, observable time-invariant city characteristics are unrelated to results. Finally, our estimates for physicians' hospital valuations are significantly correlated to more traditional hospital quality measures

Firm productivity, exchange rate movements, sources of finance and export orientation

We investigate the level and volatility effects of exchange rates on the productivity growth of manufacturing firms with heterogenous access to debt, and domestic and foreign equity markets in Turkey. We find that while exchange rate volatility affects productivity growth negatively, having access to foreign or domestic equity, or debt markets does not alleviate these effects. Furthermore, foreign owned or publicly traded companies do not appear to perform significantly better than the rest. We detect, however, that firm productivity is positively related to having access to external credit. Additionally, we find that while export (inward) oriented firms are affected less (more) by exchange rate appreciations, they are more (less) sensitive to exchange rate volatility