Causal Explanation Under Indeterminism: A Sampling Approach

By Christopher A. Merck, Samantha Kleinberg. 2016

7 citations recorded by the source corpus.

One of the key uses of causes is to explain why things happen. Explanations of specific events, like an individual’s heart attack on Monday afternoon or a particular car accident, help assign responsibility and inform our future decisions. Computational methods for causal inference make use of the vast amounts of data collected by individuals to better understand their behavior and improve their health. However, most methods for explanation of specific events have provided theoretical approaches with limited applicability. In contrast we make two main contributions: an algorithm for explanation that calculates the strength of token causes, and an evaluation based on simulated data that enables objective comparison against prior methods and ground truth. We show that the approach finds the correct relationships in classic test cases (causal chains, common cause, and backup causation) and in a realistic scenario (explaining hyperglycemic episodes in a simulation of type 1 diabetes).

Subfields: Artificial Intelligence, Endocrinology, Diabetes and Metabolism, Genetics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chao Yu
2. Jiming Liu
3. Finale Doshi-Velez
4. Taylor W. Killian
5. Guoqi Ren
6. George Dimitri Konidaris
7. Yinzhao Dong
8. Shamim Nemati

In Endocrinology, Diabetes and Metabolism

1. Guoqi Ren
2. Chao Yu
3. Jiming Liu
4. Yinzhao Dong

In Genetics

1. Finale Doshi-Velez
2. Guoqi Ren
3. George Dimitri Konidaris
4. Chao Yu
5. Jiming Liu
6. Yinzhao Dong

Citations in the corpus

• Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes (2016, background): “There have been notable efforts made in the administration of anesthesia [18], in personalizing cancer [24] and HIV therapies [11] and in understanding the causality of macro events in diabetes managment [16].”
• Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes (2017, background): “2006) and in understanding the causality of macro events in diabetes managment (Merck and Kleinberg 2015).”
• Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV (2019, methodology): “The causal factor C ( B | A ) can be computed using a sampling method proposed in [16].”