Matching Methods for Causal Inference with Time-Series Cross-Sectional Data
enforce_lead_restrictions check treatment and control units for treatm...
equality_four Small helper function implementing estimation function f...
equality_four_placebo
expand_treated_ts Builds a list that contains all times in a lag windo...
handle_conditional_se Calculates conditional standard errors analytica...
handle_mahalanobis_calculations Returns a matched.set object with weig...
DisplayTreatment
clean_leads Function to check the lead windows in treated and control ...
balance_scatter
build_maha_mats Builds the matrices that we will then use to calculate...
build_ps_data
calculate_estimates
calculate_placebo_estimates
calculate_point_estimates Helper function that calculates the point es...
check_time_data
extract_differences This function calculates the differences from t-1 ...
find_ps
findBinaryTreated
get.matchedsets
Calculate covariate balance
get_set_treatment_effects
getDits returns a vector of Dit values, as defined in the paper. They ...
getWits returns a vector of Wits, as defined in the paper (equation 25...
handle_bootstrap
handle_bootstrap_parallel
handle_bootstrap_placebo
handle_bootstrap_placebo_parallel
handle_missing_data
handle_moderating_variable handles moderating variable calculations: I...
handle_ps_match Returns a matched.set object with weights for control ...
handle_ps_weighted
handle_conditional_se Calculates conditional standard errors analytica...
identifyDirectionalChanges Identifies changes in treatment variable fo...
lwd_refinement master function that performs refinement with listwise ...
lwd_units helper function that actually subsets sets down to contain u...
matched_set
merge_formula
PanelEstimate
Matching Methods for Causal Inference with Time-Series Cross-Sectional...
PanelMatch
parse_and_prep
Prepare Control Units pcs and pts create data frames with the time/id ...
perform_refinement Performs refinement of matched sets, ultimately ret...
perunitSum This is a low level function that is used to calculate a va...
perunitSum_Dit Similar to perunitSum, this is a low level helper funct...
placebo_test
Plot the distribution of the sizes of matched sets.
Plot point estimates and standard errors from a PanelEstimate calculat...
prepare_data The calculation of point estimates and standard errors fi...
Print matched.set objects.
set_lwd_refinement Performs the set-level operations for refinement wi...
Summarize information about a matched.set object and the matched set...
Get summaries of PanelEstimate objects/calculations
Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results.