Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries in the data, SAM aims at recovering full causal models from continuous observational data along a multivariate non-parametric setting. The approach is based on a game between $d$ players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the overall joint conditional distribution, and that of the original data. An original learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the end-to-end optimization of the graph structure and parameters through stochastic gradient descent. Besides the theoretical analysis of the approach in the large sample limit, SAM is extensively experimentally validated on synthetic and real data

Releases as adverts : product discovery in video-games

Consumers do not have perfect information about products. Therefore, product discovery plays a key role. I use the market for video-games to estimate the causal effect of the release of a new game on the performance of an older game by the same company. I find that, in the weeks before the release, the old game underperforms, which is consistent with a substitution effect between the old and the new game. In the weeks after the release, the old game increases performance significantly, which suggests that there is a backwards information spillover. This average treatment effect can be as high as 20% in the number of owners. Overall, I find that the release of a new game serves as a advert for the old game.Os consumidores não têm informação perfeita sobre produtos, o que implica que a descoberta de produtos é essencial. Usando o mercado de video-jogos, eu estimo o efeito causal do lançamento de um novo jogo no sucesso de um jogo pré-existente, produzido pela mesma empresa. Nas semanas antes do lançamento, o jogo pré-existente sofre em termos de número de utilizadores, o que é consistente com um efeito de substituição entre os dois jogos. Nas semanas após o lançamento, o jogo pré-existente melhora substancialmente em termos de número de utilizadores, o que sugere que existe um efeito de transmissão de informação. O efeito médio de tratamento pode chegar a 20%. Em suma, eu encontro evidência que o lançamento de um novo jogo serve como publicidade para os jogos pré-existentes

Patterns, Information, and Causation

This paper articulates an account of causation as a collection of information-theoretic relationships between patterns instantiated in the causal nexus. I draw on Dennett’s account of real patterns to characterize potential causal relata as patterns with specific identification criteria and noise tolerance levels, and actual causal relata as those patterns instantiated at some spatiotemporal location in the rich causal nexus as originally developed by Salmon. I develop a representation framework using phase space to precisely characterize causal relata, including their degree of counterfactual robustness, causal profiles, causal connectivity, and privileged grain size. By doing so, I show how the philosophical notion of causation can be rendered in a format that is amenable for direct application of mathematical techniques from information theory such that the resulting informational measures are causal informational measures. This account provides a metaphysics of causation that supports interventionist semantics and causal modeling and discovery techniques

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar