Causal Dependence Plots

Explaining artificial intelligence or machine learning models is increasingly important. To use such data-driven systems wisely we must understand how they interact with the world, including how they depend causally on data inputs. In this work we develop Causal Dependence Plots (CDPs) to visualize how one variable--an outcome--depends on changes in another variable--a predictor--$\textit{along with any consequent causal changes in other predictor variables}$. Crucially, CDPs differ from standard methods based on holding other predictors constant or assuming they are independent. CDPs make use of an auxiliary causal model because causal conclusions require causal assumptions. With simulations and real data experiments, we show CDPs can be combined in a modular way with methods for causal learning or sensitivity analysis. Since people often think causally about input-output dependence, CDPs can be powerful tools in the xAI or interpretable machine learning toolkit and contribute to applications like scientific machine learning and algorithmic fairness

Impact Remediation: Optimal Interventions to Reduce Inequality

A significant body of research in the data sciences considers unfair discrimination against social categories such as race or gender that could occur or be amplified as a result of algorithmic decisions. Simultaneously, real-world disparities continue to exist, even before algorithmic decisions are made. In this work, we draw on insights from the social sciences and humanistic studies brought into the realm of causal modeling and constrained optimization, and develop a novel algorithmic framework for tackling pre-existing real-world disparities. The purpose of our framework, which we call the "impact remediation framework," is to measure real-world disparities and discover the optimal intervention policies that could help improve equity or access to opportunity for those who are underserved with respect to an outcome of interest. We develop a disaggregated approach to tackling pre-existing disparities that relaxes the typical set of assumptions required for the use of social categories in structural causal models. Our approach flexibly incorporates counterfactuals and is compatible with various ontological assumptions about the nature of social categories. We demonstrate impact remediation with a real-world case study and compare our disaggregated approach to an existing state-of-the-art approach, comparing its structure and resulting policy recommendations. In contrast to most work on optimal policy learning, we explore disparity reduction itself as an objective, explicitly focusing the power of algorithms on reducing inequality

Negative emotional experiences during navigation enhance parahippocampal activity during recall of place information

It is known that the parahippocampal cortex is involved in object-place associations in spatial learning, but it remains unknown whether activity within this region is modulated by affective signals during navigation. Here we used fMRI to measure the neural consequences of emotional experiences on place memory during navigation. A day before scanning, participants undertook an active object location memory task within a virtual house in which each room was associated with a different schedule of task-irrelevant emotional events. The events varied in valence (positive, negative, or neutral) and in their rate of occurrence (intermittent vs. constant). On a subsequent day, we measured neural activity while participants were shown static images of the previously learned virtual environment, now in the absence of any affective stimuli. Our results showed that parahippocampal activity was significantly enhanced bilaterally when participants viewed images of a room in which they had previously encountered negatively arousing events. We conclude that such automatic enhancement of place representations by aversive emotional events serves as an important adaptive mechanism for avoiding future threats

The Privatization Origins of Political Corporations: Evidence from the Pinochet Regime

We show that the sale of state owned firms in dictatorships can help political corporations to emerge and persist over time. Using new data, we characterize Pinochet’s privatizations in Chile and find that some firms were sold underpriced to politically connected buyers. These newly private firms benefited financially from the Pinochet regime. Once democracy arrived, they formed connections with the new government, financed political campaigns, and were more likely to appear in the Panama Papers. These findings reveal how dictatorships can influence young democracies using privatization reforms

Measurement of the Zero Crossing in a Feshbach Resonance of Fermionic 6-Li

We measure a zero crossing in the scattering length of a mixture of the two lowest hyperfine states of 6-Li. To locate the zero crossing, we monitor the decrease in temperature and atom number arising from evaporation in a CO2 laser trap as a function of magnetic field B. The temperature decrease and atom loss are minimized for B=528(4) G, consistent with no evaporation. We also present preliminary calculations using potentials that have been constrained by the measured zero crossing and locate a broad Feshbach resonance at approximately 860 G, in agreement with previous theoretical predictions. In addition, our theoretical model predicts a second and much narrower Feshbach resonance near 550 G.Comment: Five pages, four figure

On Quartet Superfluidity of Fermionic Atomic Gas

Possibility of a quartet superfluidity in fermionic systems is studied as a new aspect of atomic gas at ultra low temperatures. The four-fold degeneracy of hyperfine state and moderate coupling is indispensable for the quartet superfluidity to occur. Possible superconductivity with quartet condensation in electron systems is discussed.Comment: 7 pages, 1 figure. J. Phys. Soc. Jpn. vol.74 (2005) No.7, in press; Note added for related previous works; some typographic errors revise

Endogenous fantasy and learning in digital games.

Many people believe that educational games are effective because they motivate children to actively engage in a learning activity as part of playing the game. However, seminal work by Malone (1981), exploring the motivational aspects of digital games, concluded that the educational effectiveness of a digital game depends on the way in which learning content is integrated into the fantasy context of the game. In particular, he claimed that content which is intrinsically related to the fantasy will produce better learning than that which is merely extrinsically related. However, this distinction between intrinsic and extrinsic (or endogenous and exogenous) fantasy is a concept that has developed a confused standing over the following years. This paper will address this confusion by providing a review and critique of the empirical and theoretical foundations of endogenous fantasy, and its relevance to creating educational digital games. Substantial concerns are raised about the empirical basis of this work and a theoretical critique of endogenous fantasy is offered, concluding that endogenous fantasy is a misnomer, in so far as the "integral and continuing relationship" of fantasy cannot be justified as a critical means of improving the effectiveness of educational digital games. An alternative perspective on the intrinsic integration of learning content is described, incorporating game mechanics, flow and representations