Integrating explanation and prediction in computational social science
Computational social science is more than just large repositories of digital data and the
computational methods needed to construct and analyse them. It also represents a …
computational methods needed to construct and analyse them. It also represents a …
Statistical modeling: the three cultures
A Daoud, D Dubhashi - arXiv preprint arXiv:2012.04570, 2020 - arxiv.org
Two decades ago, Leo Breiman identified two cultures for statistical modeling. The data
modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or …
modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or …
Adjusting for confounders with text: Challenges and an empirical evaluation framework for causal inference
Causal inference studies using textual social media data can provide actionable insights on
human behavior. Making accurate causal inferences with text requires controlling for …
human behavior. Making accurate causal inferences with text requires controlling for …
MPCSL-a modular pipeline for causal structure learning
The examination of causal structures is crucial for data scientists in a variety of machine
learning application scenarios. In recent years, the corresponding interest in methods of …
learning application scenarios. In recent years, the corresponding interest in methods of …
GRFlift: uplift modeling for multi-treatment within GMV constraints
J Yang, W Wang, Y Dong, X He, L Jia, H Chen… - Applied Intelligence, 2023 - Springer
As a primary goal of predictive analytics, uplift modeling is used to estimate what impact a
specific action or treatment will have on an outcome. In convention, the treatment is …
specific action or treatment will have on an outcome. In convention, the treatment is …
The potential of benchmark challenges in the social sciences
Social scientists aim to create explanations of the world. For each social phenomenon,
scientists have proposed a myriad of theories to explain its working mechanisms …
scientists have proposed a myriad of theories to explain its working mechanisms …
[PDF][PDF] Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery
J Hügle - 2023 - researchgate.net
Abstract Knowledge about causal structures is crucial for decision support in various
domains. For example, in discrete manufacturing, identifying the root causes of failures and …
domains. For example, in discrete manufacturing, identifying the root causes of failures and …