estimate_weights: Estimate Propensity Score Weights

In PSsurvival: Propensity Score Methods for Survival Analysis

Estimate Propensity Score Weights

Description

Calculates propensity score weights for causal inference with optional trimming. Supports ATE, ATT, and overlap population estimands for binary and multiple treatment groups.

Usage

estimate_weights(
  ps_result,
  data,
  treatment_var,
  estimand = "ATE",
  att_group = NULL,
  trim = NULL,
  delta = NULL,
  alpha = NULL
)

Arguments

ps_result	A list returned by estimate_ps(), containing the fitted propensity score model and estimated propensity scores.
data	A data.frame containing the treatment variable (same data used in estimate_ps()).
treatment_var	A character string specifying the name of the treatment variable in data.
estimand	Character string specifying the target population. One of: "ATE": Average Treatment Effect (default). Uses IPW method. "ATT": Average Treatment Effect on the Treated. Uses IPW method. "overlap": Overlap population (Li & Li, 2019). Uses overlap weighting.
att_group	For ATT estimation, specifies which treatment group to target. This is MANDATORY when estimand = "ATT". Ignored for other estimands.
trim	Character string specifying the trimming method, or NULL for no trimming (default). Options: "symmetric" (Crump extension) or "asymmetric" (Sturmer extension). Trimming is NOT supported with overlap estimand.
delta	Trimming threshold for symmetric trimming in (0, 1/J], where J is the number of treatment levels. If NULL (default), uses recommended values from Yoshida et al. (2019). Ignored unless trim = "symmetric".
alpha	Percentile threshold for asymmetric trimming in (0, 0.5). If NULL (default), uses recommended values from Yoshida et al. (2019). Ignored unless trim = "asymmetric".

Details

Trimming Workflow: When trimming is requested, the function: (1) identifies observations to trim using PS from full data, (2) re-estimates PS on trimmed data, (3) calculates weights from re-estimated PS. This ensures trimming uses the original covariate distribution while weights reflect the overlapping population.

Overlap weights do not support trimming (already bounded in [0,1]).

Value

A list containing:

weights	Numeric vector of weights (length = nrow(data)).
trim_summary	Data frame with trimming summary by treatment group.
ess	Named numeric vector of effective sample size by treatment group.
method	Character string: "IPW" for ATE/ATT, "overlap" for overlap.
estimand	Character string of estimand used.
att_group	Target group for ATT (NULL if not applicable).
trim_method	Character string of trimming method (NULL if no trimming).
delta	Numeric trimming threshold used for symmetric trimming (NULL if not applicable).
alpha	Numeric percentile threshold used for asymmetric trimming (NULL if not applicable).
n_levels	Number of treatment levels.
treatment_levels	Vector of treatment level values.
ps_result	PS result object (refitted after trimming if trimming was applied).

References

Li, F., & Li, F. (2019). Propensity score weighting for causal inference with multiple treatments. The Annals of Applied Statistics, 13(4), 2389-2415.

Yoshida, K., et al. (2019). Multinomial extension of propensity score trimming methods: A simulation study. American Journal of Epidemiology, 188(3), 609-616.

Crump, R. K., et al. (2009). Dealing with limited overlap in estimation of average treatment effects. Biometrika, 96(1), 187-199.

Examples

# Example 1: Overlap weighting for binary treatment
data(simdata_bin)
ps_bin <- estimate_ps(
  data = simdata_bin,
  treatment_var = "Z",
  ps_formula = Z ~ X1 + X2 + X3 + B1 + B2
)
weights_ow <- estimate_weights(
  ps_result = ps_bin,
  data = simdata_bin,
  treatment_var = "Z",
  estimand = "overlap"
)
summary(weights_ow$weights)

# Example 2: ATT with multiple treatments
data(simdata_multi)
ps_multi <- estimate_ps(
  data = simdata_multi,
  treatment_var = "Z",
  ps_formula = Z ~ X1 + X2 + X3 + B1 + B2
)
weights_att <- estimate_weights(
  ps_result = ps_multi,
  data = simdata_multi,
  treatment_var = "Z",
  estimand = "ATT",
  att_group = "C"
)
summary(weights_att$weights)

PSsurvival documentation built on Dec. 9, 2025, 9:07 a.m.