Learning causal models that make correct manipulation predictions with time series data

One of the fundamental purposes of causal models is using them to predict the effects of manipulating various components of a system. It has been argued by Dash (2005, 2003) that the Do operator will fail when applied to an equilibrium model, unless the underlying dynamic system obeys what he calls Equilibration-Manipulation Commutability. Unfortunately, this fact renders most existing causal discovery algorithms unreliable for reasoning about manipulations. Motivated by this caveat, in this paper we present a novel approach to causal discovery of dynamic models from time series. The approach uses a representation of dynamic causal models motivated by Iwasaki and Simon (1994), which asserts that all “causation across time" occurs because a variable’s derivative has been affected instantaneously. We present an algorithm that exploits this representation within a constraint-based learning framework by numerically calculating derivatives and learning instantaneous relationships. We argue that due to numerical errors in higher order derivatives, care must be taken when learning causal structure, but we show that the Iwasaki-Simon representation reduces the search space considerably, allowing us to forego calculating many high-order derivatives. In order for our algorithm to discover the dynamic model, it is necessary that the time-scale of the data is much finer than any temporal process of the system. Finally, we show that our approach can correctly recover the structure of a fairly complex dynamic system, and can predict the effect of manipulations accurately when a manipulation does not cause an instability. To our knowledge, this is the first causal discovery algorithm that has demonstrated that it can correctly predict the effects of manipulations for a system that does not obey the EMC condition

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Policymaking Under Pressure: The Perils of Incremental Responses to Climate Change

Federal policymakers' reluctance to enact a comprehensive climate change policy during the past decade has coincided with increased awareness of the inevitability and severity of the problems from global climate change. Thus, it is no surprise that piecemeal, sub-federal policies have garnered considerable support. Bolstered by the political science literature on the promise of incrementalism and democratic experimentalism, many proponents of climate change action favor incremental steps in the hope that they will improve the environment or at least serve as a basis for more comprehensive policies. Against this hopeful view, we explain why ad hoc responses to climate change may well be no better than, and possibly will be worse than, no action at all. Incremental climate change policies can give rise to predictable and nontrivial problems, such as non-effect, leakage, climate side effects, other side effects, lock-in, and lulling. Such problems not only can undermine the interim policies themselves but also may delay the adoption of a more comprehensive climate change policy. We present an upstream cap-and-trade policy as one such comprehensive alternative, showing how it would prove less susceptible to the kinds of policy failures that afflict incremental policies. Only by resisting the pressures to act immediately, and investing the necessary time and resources to craft a comprehensive solution, will environmental policymakers be able to guard against the perils that afflict ad hoc policymaking.

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others. Still, most approaches typically address visual tasks in isolation, resulting in overspecialized models which achieve strong performances in specific applications but work poorly in other (often related) tasks. This is clearly sub-optimal for a robot which is often required to perform simultaneously multiple visual recognition tasks in order to properly act and interact with the environment. This problem is exacerbated by the limited computational and memory resources typically available onboard to a robotic platform. The problem of learning flexible models which can handle multiple tasks in a lightweight manner has recently gained attention in the computer vision community and benchmarks supporting this research have been proposed. In this work we study this problem in the robot vision context, proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art algorithms in this novel challenging scenario. We also define a new evaluation protocol, better suited to the robot vision setting. Results shed light on the strengths and weaknesses of existing approaches and on open issues, suggesting directions for future research.Comment: This work has been submitted to IROS/RAL 201

Developing Attorneys for the Future: What Can We Learn From the Fast Trackers?

Leaders in law firms tend to be those attorneys who thrive in a law firm environment from the beginning—successful associates who become successful partners. Later, they are asked to be the leaders of practice areas, committees and, ultimately, part of senior management. While high-performing associates may not be formally promoted to leadership positions for some time, it is important to understand what makes them—as young associates—stand out from their peers. Who are these future leaders, and what qualities predict their advancement in a law firm environment? These are the questions we set out to explore. To date, little empirical work exists on the characteristics and behaviors of high-potential associates—how to recognize them from the beginning and how to develop them. Instead, law students continue to be hired most commonly based on the law school they attended and their GPA, under the assumption that law school and GPA are related to future performance as an attorney. Transcript and resume review are typically accompanied by a series of 30-minute interviews consisting of questions that vary from candidate to candidate. Consequently, hiring decisions result from a combination of the reputation of the law school attended, GPA, and the interviewing partners’ gut feeling