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Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London
Our take
In the face of ongoing tube strikes in London, understanding their impact on cycling usage is crucial for urban mobility. This post delves into the application of causal inference methods to transform free-to-use data into a hypothesis-ready dataset. By analyzing how disruptions in public transport influence cycling patterns, we unlock insights that can drive better infrastructure planning and policy decisions. Join us as we explore innovative approaches to data analysis that empower urban cyclists and enhance the future of transportation in London.
Turning free-to-use data into a hypothesis-ready dataset
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