The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories

Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

The Influence of Service Quality on Customer Satisfaction and Loyalty at Pyramid Suites Hotel Banjarmasin

Aims: This study aims to analyse and describe how much influence of service quality has on customer satisfaction and loyalty. Study Design: This type of research is quantitative, with a conclusive causal approach, research that aims to obtain a conclusion from the data that obtained and processed which explains the causal relationship between the variables. Place and Duration of Study: the study was conducted at Pyramid Suites Hotel Banjarmasin, started in September 2020 until July 2021. Methodology: The population in this study are guests of Hotel Pyramid Suites who stay more than three times. The research sample was 100 respondents. The data collection technique used a questionnaire which was then used as a score tabulation for each variable studied. The data analysis method used in this study is Smart PLS 3. Results: Tangible has a negative effect on customer satisfaction of -0.411, then the t-statistic value is 2.930, this value > t-table of 1.96 and the P value is 0.004 which is smaller than 0.05. Reliability has a positive effect on customer satisfaction of 0.563, then the t-statistical value is 2.679 and the P value is 0.008. Responsiveness has a positive influence on customer satisfaction of 0.280, then the t-statistic value is 2.012, this value is > 1.96 and the P value is 0.045. Assurance has a positive effect on customer satisfaction of 0.184, then the t-statistic value is 2.684, the P value is 0.008. Empathy has a positive influence on customer satisfaction of 0.337, then the t-statistic value is 3.084, the P value is 0.002. Customer satisfaction has a positive influence on customer loyalty of 0.802, then the t-statistic value is 18.260, and the P value is 0.000. Conclusion: The results show that service quality which consist of tangible, reliability, responsiveness, assurance, empathy affect customer satisfaction and customer satisfaction affects customer loyalty

PENGARUH BOPO, NPF, ROA DAN INFLASI TERHADAP ASET PERBANKAN SYARIAH DI INDONESIA

The growth of Islamic banking assets in Indonesia has experienced a very significant growth. The purpose of this study is to analyze the internal and external factors that influence this growth. This research method uses a quantitative design with a causal approach. Internal data sources use Islamic bank financial reports for the 2016-2020 period and external data, namely inflation. The sampling technique was determined by purposive sampling method, namely 7 Islamic Commercial Banks. The results showed that the internal factors NPF and ROA had no significant effect on assets. In contrast, BOPO has a significant effect on assets. External factors, inflation have a significant effect on Islamic banking assets.

User Retention: A Causal Approach with Triple Task Modeling

For many Internet companies, it has been an important focus to improve user retention rate. To achieve this goal, we need to recommend proper services in order to meet the demands of users. Unlike conventional click-through rate (CTR) estimation, there are lots of noise in the collected data when modeling retention, caused by two major issues: 1) implicit impression-revisit effect: users could revisit the APP even if they do not explicitly interact with the recommender system; 2) selection bias: recommender system suffers from selection bias caused by user's self-selection. To address the above challenges, we propose a novel method named UR-IPW (User Retention Modeling with Inverse Propensity Weighting), which 1) makes full use of both explicit and implicit interactions in the observed data. 2) models revisit rate estimation from a causal perspective accounting for the selection bias problem. The experiments on both offline and online environments from different scenarios demonstrate the superiority of UR-IPW over previous methods. To the best of our knowledge, this is the first work to model user retention by estimating the revisit rate from a causal perspective.

Pengaruh Value Orientation dan Personality terhadap New Environmental Paradigm Siswa

The development of paradigm from anthropocentric to new environmental paradigm is necessary. Students have to possess the new environmental paradigm in order to protect the environment. Students new environmental paradigm can be affected by several factors such as value orientation and personality. This study aimed to determine the effect of value orientation and personality on the new environmental paradigm that students have. This study was hold in a first term of 2019/2020 school year at SMA Negeri 4 Kota Bekasi. The method used is the survey with quantitative-causal causal approach and path analysis. The sample is 95 students of XI MIPA which selected randomly. There were three instruments used for measuring new environmental paradigm, value orientation and personality with reability of 0.691, 0.809, and 0.864. As a result of study, personality is suitable as mediator of variables between value orientation and new environmental paradigm.

Understanding the impact of Commercial Banks Lending Rate on Economic Growth: An Empirical Evidence from Ghana.

The main objective of this paper is to investigate the impact of lending rate on economic growth in Ghana. To do this, we employ the autoregressive distributed lags model (ARDL) and the Toda and Yamamoto (1995) causal approach as estimation strategy. The estimates from the ARDL model suggest that ceteris paribus one percent increase in lending rate generates approximately 0.15 decrease in economic growth of Ghana in the long. In the short run, one percent increase in lending rate also generates approximately 0.112 percent decrease in economic growth. Contrary to the widespread belief that lending rate induce economic growth, we find that gross domestic product rather spurs lending rate, using Toda and Yamamoto (1995) causal approach. Our findings suggest that monetary authorities should embark on policy interventions that aim at taming lending rate towards growth enhancing targets. This will encourage individuals, firms and other institutions to borrow from commercial banks to increase investment and consumption to accelerate economic growth. Other policy interventions include strengthening inflation targeting policy to reduce and stabilize inflation while taming exchange rate, monetary policy and treasury bill rate towards economic growth enhancing targets.

Quantifying causal pathways of teleconnections

Teleconnections are sources of predictability for regional weather and climate but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data is usually unclear. Here we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections.

Dynamic causal communication channels between neocortical areas

Dynamic pathways of information flow between distributed brain regions underlie the diversity of behaviour. However, it remains unclear how neuronal activity in one area causally influences ongoing population activity in another, and how such interactions change over time. Here we introduce a causal approach to quantify cortical interactions by pairing simultaneous electrophysiological recordings with neural perturbations. We found that the influence visual cortical areas had on each other was surprisingly variable over time. Both feedforward and feedback pathways reliably affected different subpopulations of target neurons at different moments during processing of a visual stimulus, resulting in dynamically rotating communication dimensions between the two cortical areas. The influence of feedback on primary visual cortex (V1) became even more dynamic when visual stimuli were associated with a reward, impacting different subsets of V1 neurons within tens of milliseconds. This, in turn, controlled the geometry of V1 population activity in a behaviourally relevant manner. Thus, distributed neural populations interact through dynamically reorganizing and context- dependent communication channels to evaluate sensory information.