Konrad Paul Kording
Biographic Data
| ID | 1760370 |
|---|---|
| NAME | Konrad Paul Kording |
| GIVEN NAMES | Konrad Paul |
| FAMILY NAME | Kording |
| SIGNATURE | KORDING K P |
| AFFILIATIONS | University of Pennsylvania |
| ORCID | 0000-0001-8408-4499 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 9 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
Measuring Causal Effects of Civil Communication without Randomization
Understanding the causal effects of civility is critical when analyzing online social communication, yet measuring causality is difficult. A/B tests and other randomized experiments are the gold standard for establishing causal effects but they are inapplicable in this setting due to 1) the inability to control civility levels in an experiment, and more importantly, 2) ethical constraints on intentionally randomizing civility levels. We develop a…
Causal inference on human behaviour
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass …
Overfitting to 'predict' suicidal ideation
Quasi-experimental causality in neuroscience and behavioural research
Dopaminergic medication increases reliance on current information in Parkinson's disease
Bayesian integration in sensorimotor learning
Bayesian integration in sensorimotor learning
Dopaminergic medication increases reliance on current information in Parkinson's disease
Quasi-experimental causality in neuroscience and behavioural research
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass …
Overfitting to 'predict' suicidal ideation
Measuring Causal Effects of Civil Communication without Randomization
Understanding the causal effects of civility is critical when analyzing online social communication, yet measuring causality is difficult. A/B tests and other randomized experiments are the gold standard for establishing causal effects but they are inapplicable in this setting due to 1) the inability to control civility levels in an experiment, and more importantly, 2) ethical constraints on intentionally randomizing civility levels. We develop a…
Causal inference on human behaviour
Psychology (7 works) · Computer Science (6 works) · Cognitive psychology (4 works) · Artificial Intelligence (3 works) · Mathematics (3 works) · Medicine (3 works) · Advanced Causal Inference Techniques (2 works) · Artificial Intelligence (2 works) · Bayesian probability (2 works) · Cognitive science (2 works)