FLAME_SQLite: SQLite Database Implementation

In chiarui424/FLAME: A Fast Large-Scale Almost Matching Exactly Approach to Causal Inference

Description

FLAME_SQLite applies the FLAME algorithm based on SQLite. FLAME_SQLite does not require external database installment. However, user should connect to a temporary database with command dbConnect(SQLite(),"tempdb_name"). The required arguments include (1) db, (2) data, and (3) holdout. The rest of the arguments are optional.

Usage

FLAME_SQLite(db, data, holdout, compute_var = FALSE, tradeoff = 0.1,
  PE_function = NULL, model = NULL, ridge_reg = NULL,
  lasso_reg = NULL, tree_depth = NULL)

Arguments

db	name of the connection to temporary database
data	input data
holdout	holdout training data
compute_var	variance indicator (optional, default = FALSE)
tradeoff	Match Quality tradeoff parameter (optional, default = 0.1)
PE_function	user defined function to compute predictive error (optional)
model	user defined model - Linear, Ridge, or Lasso (optional)
ridge_reg	L2 regularization parameter if model = Ridge (optional)
lasso_reg	L1 regularization parameter if model = Lasso (optional)

Value

(1) list of covariates FLAME performs matching at each iteration, (2) Sizes, conditional average treatment effects (CATEs), and variance (if compute_var = TRUE) of matches at each iteration, (3) match quality at each iteration, and (4) the original data with additional column *matched*, indicating the number of covariates each unit is matched on. If a unit is never matched, then *matched* will be 0.

Examples

data <- data(toy_data)

db <- dbConnect(SQLite(),"tempdb")

FLAME_SQLite(db = db, data = data, holdout = data)

dbDisconnect(db)

chiarui424/FLAME documentation built on Sept. 16, 2019, 8:43 a.m.