Sample Data and Replication Code for: Mega or Micro? Influencer Selection Using Follower Elasticity

In the sample data folder, we provide a small sample of hashtags we collected from TikTok Discover page and some videos under them. In the code folder, we show how we 1) Extracted multi-modal video features from the original videos and save them into a local database (under database/generate) from which we generated the training and test data for the SVAE model (under database/output) 2) Train the SVAE model to get a 256-D latent vector representation for each video based on the learned feature weights (under SVAE) 3) Combine the content representation in the above step with other video covariates (under video_info) as the input for our causal inference (under DeepIV) 4) Estimate the DeepIV model to obtain the average and heterogeneous treatment effects (under DeepIV/treatment_effects) Finally, supplementary plots and tests are provided under DeepIV/distribution_plots and mis.