Machine Learning Regressed Causal Inference for Discrete ANM
Shuxia Lu, Jie Jiang · 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) · 2021
In this paper, Random Forest and Deep Neural Network regressed causal inference algorithms are proposed for discovering causal direction in discrete Additive Noise Model (ANM). Assuming ANM, a key step in determining causal direction is to find out the functional relation between cause and effect. This step is solved by deep-network- and random-forest-based regressions between cause and effect variables. The experiments show that Random Forest and Deep Neural Network have a good robustness and high accuracy rate in both simulated data and real data, especially Random Forest.