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Bias contour plot for ecological inference estimates

Source: R/ei_sens.R
plot.ei_sens.Rd

Displays bias bound as a function of c_outcome and c_predictor in ei_sens() on a contour plot. Bounds on the outcome, and standard errors of the point estimate, can be overlaid as contours on the plot to aid in interpretation. Benchmarked values of c_outcome and c_predictor based on the observed covariates can also be overlaid.

Usage

# S3 method for class 'ei_sens'
plot(
  x,
  y = NULL,
  predictor = NULL,
  bounds = NULL,
  bench = NULL,
  plot_se = 1:3,
  contour_exp = -2:-1,
  ...,
  lwd = 1,
  pch = 8,
  cex = 1
)

Arguments

x

An ei_sens object

y

An outcome variable, as a character vector. Defaults to first.

predictor

A predictor variable to plot, as a character vector. Defaults to first.

bounds

A vector c(min, max) of bounds for the outcome, which will affect the contours which are plotted. If bounds = NULL (the default), they will be inferred from the outcome variable: if it is contained within \([0, 1]\), for instance, then the bounds will be c(0, 1). Setting bounds = FALSE forces unbounded estimates.

bench

A data frame of benchmark values, from ei_bench(), to plot.

plot_se

A vector of multiples of the standard error to plot as contours.

contour_exp

Powers of 10 for which to plot contours of the bias bound.

...

Additional arguments passed on to contour()

lwd

Scaling factor for the contour line widths

pch

The point type (see points()) for the benchmark values, if provided

cex

Scaling factor for the benchmark points and labels, if provided

References

Chernozhukov, V., Cinelli, C., Newey, W., Sharma, A., & Syrgkanis, V. (2024). Long story short: Omitted variable bias in causal machine learning (No. w30302). National Bureau of Economic Research.

Examples

data(elec_1968)

spec = ei_spec(elec_1968, vap_white:vap_other, pres_ind_wal,
               total = pres_total, covariates = c(state, pop_urban, farm))
m = ei_ridge(spec)
rr = ei_riesz(spec, penalty = m$penalty)
est = ei_est(m, rr, spec)
sens = ei_sens(est)

plot(sens)


plot(sens, bench = ei_bench(spec), plot_se=NULL)