Interest Rate Deregulation, Bank Development And Economic Growth In South Africa: An Empirical Investigation

In this paper the dynamic relationship between interest rate reforms, bank-based financial development and economic growth is examined – using two models in a stepwise fashion. In the first model, the impact of interest rate reforms on financial development is examined using a financial deepening model. In the second model, the dynamic causal relationship between financial development and economic growth is examined, by including investment as an intermittent variable in the bi-variate setting, thereby creating a simple tri-variate causality model. Using cointegration and error-correction models, the study finds strong support for the positive impact of interest rate reforms on financial development in South Africa. However, contrary to the results from some previous studies, the study finds that financial development, which results from interest rate reforms, does not Granger cause investment and economic growth. In addition, the study finds a uni-directional causal flow from investment to financial development and prima-facie causal flow from investment to growth. The study, therefore, concludes that although interest rate reforms impact positively on financial depth in South Africa, the causal relationship between financial depth and economic growth tends to take a demand-following path. Moreover, given the causal flow from investment to financial development and a prima facie causal flow from investment to growth, it is likely that the economic development in South Africa is driven largely by the growth of the real sector rather than the financial sector

FIVE STEPS TO RESPONSIBILITY

Responsibility has entered the academic discourse of logicians hardly more than few decades ago. I suggest a logical concept of responsibility which employs ideas both from a number of theories belonging to different branches of logic as well from other academic areas. As a comment to this concept, I suggest five steps narrative scenario in order to show how the logical dimension of responsibility emerges from diverse tendencies in logic and other sciences. Here are the five steps briefly stated: Step 1. Developing modal formalisms capable of evaluative analysis of situations (deontic, epistemic and etc.). Step 2. Drawing a conceptual borderline between normal and non-normal (weak) logical systems. Step 3. Using different kinds of models. Step 4. Agent- and action- friendly turn in logic. Step 5. Creating formalisms for modeling different types of agency. An idea advocated here within 5-Steps route to responsibility is that this concept is a complex causal and evaluative (axiological) relation. A logical account may be given for causal and normative aspects of this relation. Unfolding the responsibility back and forth through 5 Steps will result in different concepts. The technicalities are minimized for the sake of keeping the philosophical scope of the paper. For the same reason I also refrain from discussing legal and juridical ramifications of the issue

Improving Evaluation Methods for Causal Modeling

Causal modeling is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. Active communities of researchers in machine learning, statistics, social science, and other fields develop and enhance algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from the experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for expanding the standard techniques for evaluating algorithms that construct causal models. Specifically, we argue for the addition of evaluation techniques that use interventional measures rather than structural or observational measures, and that evaluate with those measures on empirical data rather than synthetic data. We survey the current practice in evaluation and show that, while the evaluation techniques we advocate are rarely used in practice, they are feasible and produce substantially different results than using structural measures and synthetic data. We also provide a protocol for generating observational-style data sets from experimental data, allowing the creation of a large number of data sets suitable for evaluation of causal modeling algorithms. We then perform a large-scale evaluation of seven causal modeling methods over 37 data sets, drawn from randomized controlled trials, as well as simulators, real-world computational systems, and observational data sets augmented with a synthetic response variable. We find notable performance differences when comparing across data from different sources. This difference demonstrates the importance of using data from a variety of sources when evaluating any causal modeling methods

Efficient Causal Discovery for Robotics Applications

Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physical interactions among nearby agents and objects. Specifically, creating models to represent cause-and-effect relationships among these elements can aid in predicting unforeseen human behaviours and anticipate the outcome of particular robot actions. To be suitable for robots, causal analysis must be both fast and accurate, meeting real-time demands and the limited computational resources typical in most robotics applications. In this paper, we present a practical demonstration of our approach for fast and accurate causal analysis, known as Filtered PCMCI (F-PCMCI), along with a real-world robotics application. The provided application illustrates how our F-PCMCI can accurately and promptly reconstruct the causal model of a human-robot interaction scenario, which can then be leveraged to enhance the quality of the interaction.Comment: Published at 5th Italian Conference on Robotics and Intelligent Machines (I-RIM 3D 2023

CausaLM: Causal Model Explanation Through Counterfactual Language Models

Understanding predictions made by deep neural networks is notoriously difficult, but also crucial to their dissemination. As all ML-based methods, they are as good as their training data, and can also capture unwanted biases. While there are tools that can help understand whether such biases exist, they do not distinguish between correlation and causation, and might be ill-suited for text-based models and for reasoning about high level language concepts. A key problem of estimating the causal effect of a concept of interest on a given model is that this estimation requires the generation of counterfactual examples, which is challenging with existing generation technology. To bridge that gap, we propose CausaLM, a framework for producing causal model explanations using counterfactual language representation models. Our approach is based on fine-tuning of deep contextualized embedding models with auxiliary adversarial tasks derived from the causal graph of the problem. Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance. A byproduct of our method is a language representation model that is unaffected by the tested concept, which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at: https://amirfeder.github.io/CausaLM/ Under review for the Computational Linguistics journa

Theory-driven learning : using intra-example relationships to constrain learning

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain the learning problem. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains

What Can Be Learned from Computer Modeling? Comparing Expository and Modeling Approaches to Teaching Dynamic Systems Behavior

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single “effectiveness measure”

Producing short and long run projections for the Ecological Footprint

The Ecological Footprint is a useful tool for public awareness of ecological pressures and for policymakers who aim to reduce them. In order to determine the potential effects of future actions and policies, it is necessary to construct scenarios of future global conditions, both in the short-term and long-term. This study develops two alternative methods for creating Ecological Footprint scenarios: first using asymmetric changes in simple economic output (GDP) to look at short-term projections; then using widely accepted scenarios from international agencies to develop long-term projections. Changes in GDP were found to be causal in determining changes in the Ecological Footprint, and this method can be used for nowcasting and projecting the future Ecological Footprint. Furthermore, it was found that the projections from different agencies can be combined under a single Ecological Footprint framework, but there are certain inconsistencies across projections that are highlighted. Lastly, the use of dynamic Ecological Footprint models based on computable general equilibrium models is explored as the preferred solution for the creation of policy-relevant tools

Non-Parametric Causality Detection: An Application to Social Media and Financial Data

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about the statistical relationships among the variables of the study and can effectively control a large number of factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201