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In this project, we are trying to understand the travel behavior of people. Specifically, we are interested in the causal mechanism of how people choose a particular mode over other available modes. Ultimately, we learned which variables are causally related to each other by employing causal structure learning techniques.

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Causal Structure Learning of Mode Choice Behavior

Understanding mode choice behaviour using causal structure learning. This was a course project completed by me and Rishabh Chauhan.

This repository contains R code for the project titled "Understanding Mode Choice Behaviour through Causal Structure Learning". The part of the R code - correlation matrix for mixed data (copula estimate.R) - shared here was copied from the paper "Learning causal structure from mixed data with missing values using Gaussian copula models" by Cui et al (2019).

This R code produces two causal structure based one based on Copula PC algorithm, and the other based on Fast Causal Inference(FCI) algorithms.

Data - National Household Travel Survey 2017 - used for this can be downloaded from https://nhts.ornl.gov/assets/2016/download/Csv.zip

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In this project, we are trying to understand the travel behavior of people. Specifically, we are interested in the causal mechanism of how people choose a particular mode over other available modes. Ultimately, we learned which variables are causally related to each other by employing causal structure learning techniques.

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