Drawing causal inferences about the effect of one variable on another is something people do all the time. Machine learning (ML) and artificial intelligence (AI), in contrast, are only able to perform statistical correlations and pattern discovery. Umbrellas are associated with rain, for example, but carrying an umbrella does not make water droplets fall from the clouds. This is obvious to humans, but neither machine learning nor even classical statistics can even pose, let alone investigate, such basic assertions. One of AIХs most distinguished research groups is embarking on a mission to bring causal considerations like these to their work on ML. Founded almost 15 years ago by Geoff Hinton, the Learning in Machines and Brains program (LMB), organized and partially funded by the Canadian Institute for Advanced Research, has made causal inference a top priority for its next phase. Funds from this grant support efforts by LMB to hold a multidisciplinary conference on causal inference in AI, bringing together top experts in economics, econometrics, statistics, neuroeconomics, and logic, to discuss the challenges and opportunities in developing machine learning protocols and platforms that can detect causal relationships in data. Additional grant funds will support a series of six $50,000 ТcatalystУ grants that will spur innovation by funding targeted research projects on these and related issues.