#1124

Author: IggyJoestar

Labs/Assignment/Assignment_3_4/Group3_Assignment_3_4/Group3_Assignmente_3_4_R_Part1.R

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+install.packages("hdm")
+install.packages("xtable")
+install.packages("igraph")
+install.packages("ggraph")
+## Part 1: Double Lasso and DAGs (20 points)
+
+##Consider the US census data from the year 2015 to analyse the effect of college graduate (clg) status
+##and it’s interaction effects with gender (sex), location (mw, so,we, ne) and both on wage jointly. All
+##other variables denote some other socio-economic characteristics, e.g. marital status, occupation, and
+##experience.
+
+# Cargar los datos desde el enlace proporcionado
+file <- "https://raw.githubusercontent.com/CausalAIBook/MetricsMLNotebooks/main/data/wage2015_subsample_inference.csv"
+data <- read.csv(file)
+
+# Explorar la estructura de los datos
+str(data)
+head(data)
+
+y <- data$lwage
+Z <- subset(data, select = -c(wage, lwage))
+y_log <- log(data$wage)
+
+## A. Generate the dataset with all the two-way interactions between variables. 
+## Make sure that the categorical variables are transformed to dummies properly. 
+## Also, note that the resulting dataset contains the treatment and it’s interactions 
+## with the other variables of interest, so you don’t need to generate them separately. (2 pts)
+
+# Generar las interacciones de dos vías
+center_colmeans <- function(x) {
+  xcenter <- colMeans(x)
+  x - rep(xcenter, rep.int(nrow(x), ncol(x)))
+}
+
+controls_formula <- "~ 0 + (sex + mw + so + we + C(occ2) + C(ind2) + exp1 + exp2 + exp3 + exp4)**2"
+Zcontrols <- model.matrix(as.formula(controls_formula), data = Z)
+Zcontrols <- center_colmeans(Zcontrols)
+
+linear_het_formula <- "~ 0 + (sex + mw + so + we)"
+Zhet <- model.matrix(as.formula(linear_het_formula), data = Z)
+Zhet <- center_colmeans(Zhet)
+
+Zhet <- as.data.frame(cbind(Zhet, "clg" = Z$clg))
+nonlin_het_formula <- "~ 0 + clg + clg * (sex + mw + so + we)"
+Zinteractions <- model.matrix(as.formula(nonlin_het_formula), data = Zhet)
+interaction_cols <- Zinteractions[, grepl("clg", colnames(Zinteractions))]
+
+X <- cbind(Zinteractions, Zcontrols)
+
+## B. Use the double lasso technique to find the effect of the treatment and 
+## it’s relevant interactions on the wage. To tune the penalization parameter 
+## in the lasso step, cross-validate it. (4 pts)
+index_clg <- grep("clg", colnames(Zinteractions))
+effects_clg <- hdm::rlassoEffects(x = X, y = y, index = index_clg, post = FALSE)
+result <- summary(effects_clg)
+print(xtable(result$coef[, c(1, 2, 4)], type = "latex"), digits = 3)
+
+## C. Report a summary of the estimation of the parameters of interest. (2 pts)
+pointwise_ci <- confint(effects_clg, level = 0.95)
+print(xtable(pointwise_ci), type = "latex")
+
+joint_ci <- confint(effects_clg, level = 0.95, joint = TRUE)
+print(xtable(joint_ci), type = "latex")
+
+## D. A college degree has a positive impact on income, but the interactions 
+## with factors such as gender and geographic location show no significant 
+## differences. This indicates that the economic benefits of obtaining a degree 
+## are consistent and do not vary noticeably across the groups analyzed.
+
+## B.You are trying to study the effect of breast feeding in the number of 
+## infections a baby is likely to have. Your dataset contains the following 
+## variables : (5 pts)
+edges <- matrix(c(
+  "Individual smoking behavior", "Forced respiratory volume",
+  "Age", "Forced respiratory volume",
+  "Age", "Individual smoking behavior",
+  "Sex", "Individual smoking behavior",
+  "Sex", "Forced respiratory volume",
+  "Height", "Forced respiratory volume"
+), byrow = TRUE, ncol = 2)
+
+graph <- graph_from_edgelist(edges, directed = TRUE)
+
+ggraph(graph, layout = "fr") +
+  geom_edge_link(aes(edge_alpha = 0.7), show.legend = FALSE) +
+  geom_node_point(color = "skyblue", size = 10) +
+  geom_node_text(aes(label = name), color = "black", fontface = "bold", size = 5) +
+  theme_void() +
+  ggtitle("Directed Acyclic Graph (DAG)") +
+  theme(plot.title = element_text(size = 16, face = "bold"))
+## B.You are trying to study the effect of breast feeding in the number of 
+## infections a baby is likely to have. Your dataset contains the following 
+## variables : (5 pts)
+
+edges <- matrix(c(
+  "Breast fed", "Number of infections",
+  "Marital status", "Family income",
+  "Family income", "Breast fed",
+  "Family income", "Childcare outside home",
+  "Education", "Family income",
+  "Number of children", "Breast fed",
+  "Childcare outside home", "Number of infections"
+), byrow = TRUE, ncol = 2)
+
+graph <- graph_from_edgelist(edges, directed = TRUE)
+
+ggraph(graph, layout = "fr") +
+  geom_edge_link(aes(edge_alpha = 0.7), show.legend = FALSE) +
+  geom_node_point(color = "skyblue", size = 10) +
+  geom_node_text(aes(label = name), color = "black", fontface = "bold", size = 5) +
+  theme_void() +
+  ggtitle("Directed Acyclic Graph (DAG)") +
+  theme(plot.title = element_text(size = 16, face = "bold"))
+
+
+