Marginal integration for nonparametric causal inference

We consider the problem of inferring the total causal effect of a single variable intervention on a (response) variable of interest. We propose a certain marginal integration regression technique for a very general class of potentially nonlinear structural equation models (SEMs) with known structure, or at least known superset of adjustment variables: we call the procedure S-mint regression. We easily derive that it achieves the convergence rate as for nonparametric regression: for example, single variable intervention effects can be estimated with convergence rate $n^{-2/5}$ assuming smoothness with twice differentiable functions. Our result can also be seen as a major robustness property with respect to model misspecification which goes much beyond the notion of double robustness. Furthermore, when the structure of the SEM is not known, we can estimate (the equivalence class of) the directed acyclic graph corresponding to the SEM, and then proceed by using S-mint based on these estimates. We empirically compare the S-mint regression method with more classical approaches and argue that the former is indeed more robust, more reliable and substantially simpler.Comment: 40 pages, 14 figure

Developing ontologies in OWL: An observational study

No abstract

New tests of local Lorentz invariance of gravity with small-eccentricity binary pulsars

In the post-Newtonian parametrization of semi-conservative gravity theories, local Lorentz invariance (LLI) violation is characterized by two parameters, alpha_1 and alpha_2. In binary pulsars the isotropic violation of LLI in the gravitational sector leads to characteristic preferred frame effects (PFEs) in the orbital dynamics, if the barycenter of the binary is moving relative to the preferred frame with a velocity w. For small-eccentricity binaries, the effects induced by alpha_1 and alpha_2 decouple, and can therefore be tested independently. We use recent timing results of two compact pulsar-white dwarf binaries with known 3D velocity, PSRs J1012+5307 and J1738+0333, to constrain PFEs for strongly self-gravitating bodies. We derive a limit |alpha_2| < 1.8e-4 (95% CL), which is the most constraining limit for strongly self-gravitating systems up to now. Concerning alpha_1, we propose a new, robust method to constrain this parameter. Our most conservative result, alpha_1 = -0.4^{+3.7}_{-3.1} e-5 (95% CL) from PSR J1738+0333, constitutes a significant improvement compared to current most stringent limits obtained both in Solar system and binary pulsar tests. We also derive corresponding limits for alpha_1 and alpha_2 for a preferred frame that is at rest with respect to our Galaxy, and preferred frames that locally co-move with the rotation of our Galaxy. (Abridged)Comment: 34 pages, 8 figures, 2 tables; accepted by Classical and Quantum Gravit

Can we learn anything from economic geography proper?.

This paper considers the ways geographers (proper) and (geographical) economists approach the study of economic geography. It argues that there are two areas where the approach of the latter is more robust than the former. First, formal models identify which assumptions are crucial in obtaining a particular result and enforce internal consistency when moving from micro to macro behaviour. Second, empirical work tends to be more rigorous. There is much greater emphasis on identifying and testing refutable predictions from theory and on dealing with issues of observational equivalence. But any approach can be improved and so the paper also identifies ways in which geographical economists could learn from the direction taken by economic geographers proper.

Can We Learn Anything from Economic Geography Proper?

Abstract This paper considers the ways geographers (proper) and (geographical) economists approach the study of economic geography. It argues that there are two areas where the approach of the latter is more robust than the former. First, formal models both enforce internal consistency and allow one to move from micro to macro behaviour. Second, empirical work tends to be more rigorous, emphasising the importance of getting representative samples, testing whether findings are significant, identifying and testing empirical predictions from theory and dealing with issues of observational equivalence. But any approach can be improved and so the paper also identifies ways in which geographical economists could learn from the direction taken by economic geographers proper.Economic geography, geographical economics, regional science, relational economic geography

Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results. In this work, we show that---surprisingly---the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. More specifically, we prove that for observational data generated by a simple and $\sigma$-faithful Structural Causal Model (SCM), FCI is sound and complete, and can be used to consistently estimate (i) the presence and absence of causal relations, (ii) the presence and absence of direct causal relations, (iii) the absence of confounders, and (iv) the absence of specific cycles in the causal graph of the SCM. We extend these results to constraint-based causal discovery algorithms that exploit certain forms of background knowledge, including the causally sufficient setting (e.g., the PC algorithm) and the Joint Causal Inference setting (e.g., the FCI-JCI algorithm).Comment: Major revision. To appear in Proceedings of the 36 th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 202