add missing simulation code

[ml_measurement_error_public.git] / simulations / example_3.R

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+### EXAMPLE 2_b: demonstrates how measurement error can lead to a type sign error in a covariate
+### What kind of data invalidates fong + tyler?
+### This is the same as example 2, only instead of x->k we have k->x.
+### Even when you have a good predictor, if it's biased against a covariate you can get the wrong sign.
+### Even when you include the proxy variable in the regression.
+### But with some ground truth and multiple imputation, you can fix it.
+
+library(argparser)
+library(mecor)
+library(ggplot2)
+library(data.table)
+library(filelock)
+library(arrow)
+library(Amelia)
+library(Zelig)
+library(predictionError)
+options(amelia.parallel="no",
+        amelia.ncpus=1)
+setDTthreads(40)
+
+source("simulation_base.R")
+
+## SETUP:
+### we want to estimate x -> y; x is MAR
+### we have x -> k; k -> w; x -> w is used to predict x via the model w.
+### A realistic scenario is that we have an NLP model predicting something like "racial harassment" in social media comments
+### The labels x are binary, but the model provides a continuous predictor
+
+### simulation:
+#### how much power do we get from the model in the first place? (sweeping N and m)
+#### 
+
+## one way to do it is by adding correlation to x.obs and y that isn't in w.
+## in other words, the model is missing an important feature of x.obs that's related to y.
+simulate_latent_cocause <- function(N, m, B0, Bxy, Bgy, Bkx, Bgx, seed){
+    set.seed(seed)
+
+    ## the true value of x
+
+    g <- rbinom(N, 1, 0.5)
+
+    # make w and y dependent
+    u <- rnorm(N,0,Bxy)
+
+    xprime <- Bgx * g + rnorm(N,0,1) 
+
+    k <- Bkx*xprime + rnorm(N,0,1.5) + 1.1*Bkx*u
+
+    x <- as.integer(logistic(scale(xprime)) > 0.5)
+
+    y <-  Bxy * x  + Bgy * g + rnorm(N, 0, 1) + B0 + u
+
+    df <- data.table(x=x,k=k,y=y,g=g)
+
+    w.model <- glm(x ~ k,df, family=binomial(link='logit')) 
+
+    if( m < N){
+        df <- df[sample(nrow(df), m), x.obs := x]
+    } else {
+        df <- df[, x.obs := x]
+    }
+
+    df[, x.obs := x.obs]
+
+    w <- predict(w.model, df) + rnorm(N, 0, 1)
+    ## y = B0 + B1x + e
+
+    df[,':='(w=w, w_pred = as.integer(w>0.5),u=u)]
+    return(df)
+}
+
+## simulate_latent_cocause_2 <- function(N, m, B0, Bxy, Bgy, Bkx, Bgx, seed){
+##     set.seed(seed)
+
+##     ## the true value of x
+
+##     g <- rbinom(N, 1, 0.5)
+
+##     # make w and y dependent
+##     u <- rnorm(N,0,5)
+
+##     xprime <- Bgx * g + rnorm(N,0,1)
+
+##     k <- Bkx*xprime + rnorm(N,0,3)
+
+##     x <- as.integer(logistic(scale(xprime+0.3)) > 0.5)
+
+##     y <-  Bxy * x  + Bgy * g + rnorm(N, 0, 1) + B0 + u
+
+##     df <- data.table(x=x,k=k,y=y,g=g)
+
+##     w.model <- glm(x ~ k, df, family=binomial(link='logit')) 
+
+##     if( m < N){
+##         df <- df[sample(nrow(df), m), x.obs := x]
+##     } else {
+##         df <- df[, x.obs := x]
+##     }
+
+##     w <- predict(w.model,data.frame(k=k)) + u 
+##     ## y = B0 + B1x + e
+
+##     df[,':='(w=w, w_pred = as.integer(w>0.5),u=u)]
+##     return(df)
+## }
+
+
+schennach <- function(df){
+
+    fwx <- glm(x.obs~w, df, family=binomial(link='logit'))
+    df[,xstar_pred := predict(fwx, df)]
+    gxt <- lm(y ~ xstar_pred+g, df)
+
+}
+
+
+parser <- arg_parser("Simulate data and fit corrected models")
+parser <- add_argument(parser, "--N", default=5000, help="number of observations of w")
+parser <- add_argument(parser, "--m", default=200, help="m the number of ground truth observations")
+parser <- add_argument(parser, "--seed", default=432, help='seed for the rng')
+parser <- add_argument(parser, "--outfile", help='output file', default='example_2.feather')
+args <- parse_args(parser)
+
+B0 <- 0
+Bxy <- 0.2
+Bgy <- 0
+Bkx <- 2
+Bgx <- 0
+
+
+outline <- run_simulation(simulate_latent_cocause(args$N, args$m, B0, Bxy, Bgy, Bkx, Bgx, args$seed)
+                         ,list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy,'Bgy'=Bgy, 'Bkx'=Bkx, 'Bgx'=Bgx, 'seed'=args$seed))
+
+outfile_lock <- lock(paste0(args$outfile, '_lock'),exclusive=TRUE)
+if(file.exists(args$outfile)){
+    logdata <- read_feather(args$outfile)
+    logdata <- rbind(logdata,as.data.table(outline))
+} else {
+    logdata <- as.data.table(outline)
+}
+
+print(outline)
+write_feather(logdata, args$outfile)
+unlock(outfile_lock)