# synthetizeCausalEffect: Computes Average Causal Effects by Covariate Adjustment in... In CausalFX: Methods for Estimating Causal Effects from Observational Data

## Description

Computes the average causal effect (ACE) of a given treatment variable X on a given outcome Y for the models generated by `simulateWitnessModel`. This assumes the synthetic models are small enough, as adjustment is done by brute force calculation.

## Usage

 `1` ```synthetizeCausalEffect(problem) ```

## Arguments

 `problem` a `cfx` problem instance for the ACE of a given treatment X on a given outcome Y. This problem instance should have a fully specified causal model, including a graph and conditional probability tables. It must also be small enough so that the joint probability must have been pre-computed.

## Details

The algorithm implemented is a naive one. When creating the `cfx` object, field `num_v_max` must be large enough so that the joint distribution is computed in advance. Only for relatively small models (approximately 20 variables in total) this will be feasible.

## Value

A list containing three different types of estimand:

 `effect_real` the true ACE. `effect_naive` the result of a naive adjustment using all of the observed covariates. `effect_naive2` the result of a naive adjustment using no covariates.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## Generate a synthetic problem problem <- simulateWitnessModel(p = 4, q = 4, par_max = 3, M = 1000) ## Idealized case: suppose we know the true distribution, ## get "exact" ACE estimands for different adjustment sets sol_pop <- covsearch(problem, pop_solve = TRUE) effect_pop <- synthetizeCausalEffect(problem) cat(sprintf( "ACE (true) = %1.2f\nACE (adjusting for all) = %1.2f\nACE (adjusting for nothing) = %1.2f\n", effect_pop\$effect_real, effect_pop\$effect_naive, effect_pop\$effect_naive2)) ```

CausalFX documentation built on May 29, 2017, 6:34 p.m.