Summary of A Dynamical View Of the Question Of Why, by Mehdi Fatemi and Sindhu Gowda
A Dynamical View of the Question of Why
by Mehdi Fatemi, Sindhu Gowda
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to address causal reasoning in multivariate time series data generated by stochastic processes. Unlike existing methods that are largely restricted to static settings, this paradigm directly establishes causation between events across time. The authors present two key lemmas to compute causal contributions and frame them as reinforcement learning problems. This framework offers formal and computational tools for uncovering and quantifying causal relationships in diffusion processes, which subsume various important settings such as discrete-time Markov decision processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how things happen in a series of events over time. Right now, most methods only look at one moment in time, but this approach considers the whole sequence of events and tries to figure out what’s causing what to happen. The authors came up with two important ideas that help them do this and they use a special type of learning called reinforcement learning. This framework can help us understand complex processes like diffusion, which is important for things like predicting how diseases spread. |
Keywords
* Artificial intelligence * Diffusion * Reinforcement learning * Time series
