rmpw {rmpw}

Causal Mediation Analysis Using Weighting Approach

Description

Causal Mediation Analysis Using Weighting Approach

Usage

rmpw(data, treatment, mediator, outcome, propensity_x, outcome_x, decomposition)

Arguments

data

The data set for analysis.

treatment

The name of the treatment variable (string).

mediator

The name of the mediator variable (string).

outcome

The name of the outcome variable (string).

propensity_x

A vector of variable names (string) of pretreatment confounders, which will be included in the propensity score model.

outcome_x

A vector of variable names (string) of pretreatment confounders, which will be included in the outcome model.

decomposition

Type of decomposition. When decomposition = 1, the total treatment effect will be decomposed into pure direct effect (DE.0), total and pure indirect effect (IE.1 and IE.0), and natural treatment-by-mediator interaction effect (IE.1 - IE.0). When decomposition = 2, the total treatment effect will be decomposed into pure indirect effect (IE.0), total and pure direct effect (DE.1 and DE.0), and natural treatment-by-mediator interaction effect (DE.1 - DE.0).

Value

A list contains the estimates of the causal effects and the coefficients of the pretreatment covariates.

Author(s)

Xu Qin and Guanglei Hong

References

Hong, G., Deutsch, J., & Hill, H. D. (2015). Ratio-of-mediator-probability weighting for causal mediation analysis in the presence of treatment-by-mediator interaction. Journal of Educational and Behavioral Statistics, 40 (3), 307-340. doi:10.3102/1076998615583902

Examples

data(Riverside)
rmpw(data = Riverside, treatment = "treat", mediator = "emp", outcome = "trunc_dep12sm2",
    propensity_x = c("emp_prior", "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53",
        "pqtrunc30", "hispanic", "pqtrunc49", "nevmar"), outcome_x = c("emp_prior",
        "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic",
        "pqtrunc49", "nevmar"), decomposition = 0)
rmpw(data = Riverside, treatment = "treat", mediator = "emp", outcome = "trunc_dep12sm2",
    propensity_x = c("emp_prior", "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53",
        "pqtrunc30", "hispanic", "pqtrunc49", "nevmar"), outcome_x = c("emp_prior",
        "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic",
        "pqtrunc49", "nevmar"), decomposition = 1)
rmpw(data = Riverside, treatment = "treat", mediator = "emp", outcome = "trunc_dep12sm2",
    propensity_x = c("emp_prior", "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53",
        "pqtrunc30", "hispanic", "pqtrunc49", "nevmar"), outcome_x = c("emp_prior",
        "pqtrunc50", "pqtrunc51", "pqtrunc52", "pqtrunc53", "pqtrunc30", "hispanic",
        "pqtrunc49", "nevmar"), decomposition = 2)