Struct SensitivityParams 

pub struct SensitivityParams {
    pub cf_y: f64,
    pub cf_d: f64,
    pub rho: f64,
    pub level: f64,
    pub null_hypothesis: Vec<f64>,
}

A confounding scenario for DmlFit::sensitivity_analysis.

Fields

cf_y: f64

cf_d: f64

rho: f64

level: f64

null_hypothesis: Vec<f64>

One null hypothesis per treatment, or a single value for all.

Implementations

impl SensitivityParams

pub fn new() -> Self

pub fn cf_y(self, cf_y: f64) -> Self

Share of the outcome’s residual variation explained by the confounder.

pub fn cf_d(self, cf_d: f64) -> Self

Relative gain in the Riesz representer’s variation from the confounder.

pub fn rho(self, rho: f64) -> Self

Correlation of the two confounding channels, in [−1, 1].

pub fn level(self, level: f64) -> Self

Level of the one-sided confidence bounds.

pub fn null_hypothesis(self, value: f64) -> Self

pub fn null_hypotheses(self, values: Vec<f64>) -> Self

Trait Implementations

impl Clone for SensitivityParams

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for SensitivityParams

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for SensitivityParams

fn default() -> Self

DoubleML’s defaults: c_y = c_d = 0.03, ρ = 1, level 0.95, H0: θ = 0.

impl PartialEq for SensitivityParams

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for SensitivityParams