simulate_bounds {causaloptim} | R Documentation |

Run a simple simulation based on the bounds. For each simulation, sample the set of counterfactual probabilities from a uniform distribution, translate into a multinomial distribution, and then compute the objective and the bounds in terms of the observable variables.

simulate_bounds(obj, bounds, nsim = 1000)

`obj` |
Object as returned by analyze_graph |

`bounds` |
Object as returned by optimize_effect |

`nsim` |
Number of simulation replicates |

A data frame with columns: objective, bound.lower, bound.upper

b <- graph_from_literal(X -+ Y, Ur -+ X, Ur -+ Y) V(b)$leftside <- c(0,0,0) V(b)$latent <- c(0,0,1) E(b)$rlconnect <- E(b)$edge.monotone <- c(0, 0, 0) obj <- analyze_graph(b, constraints = NULL, effectt = "p{Y(X = 1) = 1} - p{Y(X = 0) = 1}") bounds <- optimize_effect(obj) simulate_bounds(obj, bounds, nsim = 5)

[Package *causaloptim* version 0.8.2 Index]