Causal Effect Inference with Deep Latent-Variable Models

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.Comment: Published as a conference paper at NIPS 201

Estimating an SME investment gap and the contribution of financing frictions. ESRI WP589, March 2018

In this paper, we use firm-level survey data to explore the determinants of SME investment activity and the extent to which observed investment is in line with that suggested by economic fundamentals. In contrast to previous literature which has focused on whether investment gaps exist at a more aggregate level, we find evidence that for SMEs actual investment is below what would be expected given how companies are currently performing. The estimated magnitude of this investment gap is economically meaningful at just over 30 per cent in 2016. We explore the extent to which the gap is explained by financial market challenges such as access to finance, interest rates, and the availability of collateral. Financing frictions are found to account for a moderate share of the overall investment gap (between 10 per cent and 20 per cent of the gap)

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution