Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.Comment: 101 pages, second revision submitted to Journal of Machine Learning Researc

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Financial constraints and exports: evidence from Portuguese manufacturing firms

This paper analyzes the links between financial constraints and firm export behavior, at the firm level, by using data on Portuguese manufacturing enterprises. Theoretical models of Chaney (2005) and Manova (2010) suggest that credit constraints are detrimental for exports but no model explains consistently why exports could improve firms´ financial health. Previous empirical literature has not yet reached a consensus on these subjects and there is a great heterogeneity in measuring financial constraints and how to assess the causality relationships; results are also quite heterogeneous. Developing a very recent trend, we approximate credit constraints by using a financial score built on eight variables; to assess the effects of exports on the financial status of firms we apply, for the first time to these types of studies, a propensity score matching with difference in differences. This procedure is used to deal with the endogeneity problems, stemming from the fact that new exporters have most likely initial better financial health. We find that firms enjoying better financial health are more likely to become exporters and that new exporters show improvements in their financial situation. These findings have important policy implications as they suggest that public intervention to support exports is clearly justified.exports, matching, financial constraints, corporate finances