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TSCSMethods.jl

Matching methods for causal inference with time-series cross-sectional data

TSCSMethods.jl v2.0.1 implements the matching methodology developed in Feltham et al. (2023), which is based on Imai et al. (2021) for causal inference with time-series cross-sectional (TSCS) data.

This package was initially developed for and used in the analyses of Feltham et al. (2023).

Quick Start

using TSCSMethods, DataFrames

# Generate example data
dat = example_data(n_units=50, n_days=60)

# Create model
model = makemodel(
    dat,              # Your data
    :day,             # Time variable
    :fips,            # Unit identifier  
    :gub,             # Treatment variable
    :death_rte,       # Outcome variable
    [:pop_dens],      # Covariates for matching
    Dict(:pop_dens => false),  # Which covariates are time-varying
    5:10,             # F: Post-treatment periods to estimate
    -15:-10           # L: Pre-treatment periods for matching
)

### Key Parameters Explained

- **F periods** (`5:10`): How many periods after treatment to estimate effects
- **L periods** (`-15:-10`): Which pre-treatment periods to use for matching
  - **Must be negative** for pre-treatment
  - Used to find similar control units
- **Time-varying covariates**: Set `true` if covariate changes over time

# Run complete workflow
match!(model, dat)      # Find matched control units
balance!(model, dat)    # Calculate covariate balances  
estimate!(model, dat; dobayesfactor=false)   # Estimate treatment effects

# View results
model.results

Key Features

  • Staggered Treatment Design: Handles units treated at different times
  • Matching & Balancing: Find comparable control units and assess covariate balance
  • Bootstrap Inference: Robust standard errors and confidence intervals
  • Time-Series Structure: Explicitly accounts for temporal correlation
  • Multiple Outcomes: Support for analyzing multiple dependent variables

Installation

using Pkg
Pkg.add(url="https://github.com/human-nature-lab/TSCSMethods.jl")

Citation

If you use TSCSMethods.jl in your research, please cite:

@article{feltham_mass_2023,
  title={Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021},
  author={Feltham, Eric and Forastiere, Laura and Alexander, Marcus and Christakis, Nicholas A},
  journal={Nature Human Behaviour},
  year={2023},
  publisher={Nature Publishing Group}
}

@misc{feltham_tscsmethods_2023,
  title={TSCSMethods.jl: Matching methods for causal inference with time-series cross-sectional data},
  author={Feltham, Eric Martin},
  year={2023},
  url={https://github.com/human-nature-lab/TSCSMethods.jl}
}

Please also cite the foundational methodology:

@article{imai_matching_2021,
  title={Matching Methods for Causal Inference with Time-Series Cross-Sectional Data},
  author={Imai, Kosuke and Kim, In Song and Wang, Erik H},
  journal={American Journal of Political Science},
  year={2021},
  publisher={Wiley Online Library}
}

References

  • Imai, K., Kim, I. S., & Wang, E. H. (2021). Matching Methods for Causal Inference with Time-Series Cross-Sectional Data. American Journal of Political Science.
  • Feltham, E., Forastiere, L., Alexander, M., & Christakis, N. A. (2023). Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021. Nature Human Behaviour.
  • Kim, I. S., Ruah, A., Wang, E., & Imai, K. (2020). Insongkim/PanelMatch [R, C]. https://github.com/insongkim/PanelMatch (Original work published 2018)