The Causal Nature of Modeling with Big Data
Bibliographic Data
| ID | 19563109 |
|---|---|
| Authors | Wolfgang Pietsch (0000-0002-3058-5623, Technical University of Munich, corresponding author) |
| Year | 2016 |
| Volume | 29 |
| Issue | 2 |
| Pages | 137-171 |
| Publication date | 2016-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy & Technology (JOURNAL) |
| Journal identifiers | ISSN: 2210-5433 • E-ISSN: 2210-5441 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13347-015-0202-2 |
| OpenAlex | W2133164147 |
| Language | EN |
| Citations received | 14 |
| References cited | 39 |
Epistemology · Philosophy of science · Philosophy of technology · Data Visualization and Analytics · Philosophy · Philosophy and History of Science · Scientific Computing and Data Management
Theorizing about consciousness
Theorizing about consciousness
Financing pathways for the ageing industry
Explanatory pragmatism
Artificial intelligence and big data-driven evaluation research and practices
Prediction via Similarity
Algorithmic Accountability and Public Reason
The Fate of Explanatory Reasoning in the Age of Big Data
Causal scientific explanations from machine learning
The importance of expert knowledge in big data and machine learning
Big data and prediction
Understanding climate phenomena with data-driven models
Varieties of Data-Centric Science
Big Data, epistemology and causality
How the Laws of Physics Lie
Causation, Prediction, and Search
Causation and Explanation
Causality
The Aim and Structure of Physical Theory
Causation, Prediction, and Search
Extending Ourselves
Statistical Modeling
Causation
The Unreasonable Effectiveness of Data
Structural Realism
The Dappled World
Preliminary discourse on the study of natural philosopphy
Representing and Intervening
Causality and Causal Modelling in the Social Sciences
Big Data and Their Epistemological Challenge
The Victory Lab
Getting rid of interventions
Introduction
Integrating data to acquire new knowledge
The Dawn of Statistical ASR and MT
Science without (parametric) models
Understanding, explanation, and unification
Aspects of Theory-Ladenness in Data-Intensive Science
Entering New Fields
Representing and intervening
The cement of the universe
Big Data, new epistemologies and paradigm shifts
Three maps and three misunderstandings
| Citations per year | 1.4 |
|---|---|
| Citation span | 2016 - 2025 (10) |
| Highly cited | No |