DataSimClass-Hainmueller: Hainmueller data example

In ericdunipace/causalOT: Optimal Transport Weights for Causal Inference

Hainmueller data example

Description

Hainmueller data example

Hainmueller data example

Details

Generates the data as described in Hainmueller (2012).

Value

An R6 object of class DataSim

Super class

causalOT::DataSim -> Hainmueller

Methods

Public methods

• Hainmueller$gen_data()

• Hainmueller$gen_x()

• Hainmueller$gen_y()

• Hainmueller$gen_z()

• Hainmueller$new()

• Hainmueller$get_design()

• Hainmueller$get_pscore()

• Hainmueller$clone()

Inherited methods

• causalOT::DataSim$get_n()
• causalOT::DataSim$get_p()
• causalOT::DataSim$get_tau()
• causalOT::DataSim$get_x()
• causalOT::DataSim$get_x0()
• causalOT::DataSim$get_x1()
• causalOT::DataSim$get_y()
• causalOT::DataSim$get_z()

---

Method gen_data()

Generates the data

Usage

Hainmueller$gen_data()

---

Method gen_x()

Generates the covaraiate data

Usage

Hainmueller$gen_x()

---

Method gen_y()

Generates the outcome data

Usage

Hainmueller$gen_y()

---

Method gen_z()

Generates the treatment indicator

Usage

Hainmueller$gen_z()

---

Method new()

Generates the the Hainmueller R6 class

Usage

Hainmueller$new(
  n = 100,
  p = 6,
  param = list(),
  design = "A",
  overlap = "low",
  ...
)

Arguments

n

The number of observations

p

The dimensions of the covariates. Fixed to 6.

param

The data generating parameters fed as a list.

design

One of "A" or "B". See details.

overlap

One of "high", "low", or "medium". See details.

...

Extra arguments. Currently unused.

Details

Design

Design "A" is the setting where the outcome is generated from a linear model, Y(0) = Y(1) = X_1 + X_2 + X_3 - X_4 + X_5 + X_6 + \eta and design "B" is where the outcome is generated from the non-linear model Y(0) = Y(1) = (X_1 + X_2 +X_5 )^2 + \eta.

Overlap

The treatment indicator is generated from Z = 1(X_1 + 2 X_2 - 2 X_3 - X_4 - 0.5 X_5 + X_6 + \nu > 0), where \nu depends on the overlap selected. If overlap is "high", then \nu \sim N(0, 100). If overlap is "low", then \nu \sim N(0, 30). Finally, if overlap is "medium", then \nu is drawn from a \chi^2 with 5 degrees of freedom that is scaled and centered to have mean 0.5 and variance 67.6.

Returns

An object of class DataSim.

Examples

data <- Hainmueller$new(n = 100, p = 6, design = "A", overlap = "low")
data$gen_data()
print(data$get_x()[1:2,])

---

Method get_design()

Returns the chosen design parameters

Usage

Hainmueller$get_design()

---

Method get_pscore()

Returns the true propensity score

Usage

Hainmueller$get_pscore()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Hainmueller$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `Hainmueller$new`
## ------------------------------------------------

data <- Hainmueller$new(n = 100, p = 6, design = "A", overlap = "low")
data$gen_data()
print(data$get_x()[1:2,])

ericdunipace/causalOT documentation built on Aug. 8, 2024, 6:14 p.m.