agrega codigo base para inferencia causal

notas/09-bases-inferencia-causal.Rmd

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+# Inferencia causal
+
+
+```{r setup, include=FALSE, message=FALSE}
+library(tidyverse)
+library(cmdstanr)
+library(rstanarm)
+library(arm)
+library(bayesplot)
+library(loo)
+
+library(patchwork)
+library(scales)
+knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning=FALSE, 
+                      fig.align = 'center', fig.width = 5, fig.height=3, cache = TRUE)
+comma <- function(x) format(x, digits = 2, big.mark = ",")
+theme_set(theme_linedraw())
+color.blues <- c(NA,"#BDD7E7", "#6BAED6", "#3182BD", "#08519C", "#074789", "#063e77", "#053464")
+color.itam  <- c("#00362b", "#00503f", "#006953", "#008367", "#009c7b", "#00b68f")
+
+
+sin_lineas <- theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
+sin_leyenda <- theme(legend.position = "none")
+sin_ejes <- theme(axis.ticks = element_blank(), 
+                  axis.text = element_blank())
+
+options(mc.cores = 4)
+```
+
+```{r}
+
+data <- tibble(female = factor(rep(rep(c(1,0), each = 2), 2)), 
+       age    = rep(c(4,5,6,7), each = 2) * 10, 
+       treatment = factor(rep(c(0,1), each = 4)), 
+       outcome = rep(c(140, 150, 155, 160), each = 2))
+
+model.1 <- stan_glm(outcome ~ treatment, data = data, refresh = 0)
+
+summary(model.1)
+
+```
+```{r}
+
+model.2 <- stan_glm(outcome ~ female + treatment + treatment:female, 
+                    data = data, 
+                    refresh = 0)
+model.2
+summary(model.2)
+```
+
+```{r}
+
+data <- data %>% 
+    mutate(treatment.rand = rep(c(0,1), 4))
+
+model.3 <- stan_glm(outcome ~ female + treatment.rand + treatment.rand:female, 
+                    data = data, 
+                    refresh = 0)
+
+summary(model.3)
+```
+