Causal Inference

Estimating causal effects from observational and interventional data

My research investigates the causal effects of social media interventions on the spread of online misinformation.

  1. Identifying the Causal Effects of Twitter’s interventions on Trump’s tweets slides

"Interventions" were applied to President Donald Trump's tweets during the period following the 2020 election

  1. Identifying the Causal Effect of the Reduction of Feedback on the Sharing of Low-Quality News Online

Causal Effects of Interventions on Misinformation

I am interested in examining the impact of interventions taken by social networks to limit the spread of misinformation. In recent work at NYU Center for Social Media and Politics, I extend the analysis of Sanderson et. al, 2021 to estimate the causal effects of warning labels and tweet removal that Twitter performed on Donald Trump. Going beyond what platforms are able to do, as external researchers, we studied the effects that Twitter’s interventions had on the sharing of the labeled tweets outside of Twitter, i.e. on Facebook, Instagram, and Reddit. The results were presented at the Stanford Internet Observatory’s first Trust and Safety Research Conference, 2022.

Naive estimates may mislead us into believing that interventions don't work, but in reality they did work out--read the paper to learn why!

Causal effects are typically challenging to estimate in a network context and almost always limited to individual platforms, so I’m excited to be able to move the needle on approaches to identify novel interventions and use synthetic counterfactuals to estimate the average treatment effect in temporal settings.