causal_estimate
Classes
- CausalEstimate – Result container for causal effect estimates.
CausalEstimate
Bases: BaseModel
Result container for causal effect estimates.
Parameters
- estimand (
str) – The estimand being estimated (e.g., 'ATE', 'ATTE', 'CATE'). - model (
str) – The name of the model used for estimation. - model_options (
dict) – Options passed to the model. - value (
float) – The estimated absolute effect. - ci_upper_absolute (
float) – Upper bound of the absolute confidence interval. - ci_lower_absolute (
float) – Lower bound of the absolute confidence interval. - value_relative (
float) – The estimated relative effect. - ci_upper_relative (
float) – Upper bound of the relative confidence interval. - ci_lower_relative (
float) – Lower bound of the relative confidence interval. - alpha (
float) – The significance level (e.g., 0.05). - p_value (
float) – The p-value from the test. - is_significant (
bool) – Whether the result is statistically significant at alpha. - n_treated (
int) – Number of units in the treatment group. - n_control (
int) – Number of units in the control group. - treatment_mean (
float) – Mean outcome in the treatment group. - control_mean (
float) – Mean outcome in the control group. - outcome (
str) – The name of the outcome variable. - treatment (
str) – The name of the treatment variable. - confounders (
list of str) – The names of the confounders used in the model. - time (
str) – The date when the estimate was created (YYYY-MM-DD). - diagnostic_data (
DiagnosticData) – Additional diagnostic data_contracts.
Functions
- summary – Return a summary DataFrame of the results.
alpha
ci_lower_absolute
ci_lower_relative
ci_upper_absolute
ci_upper_relative
confounders
control_mean
diagnostic_data
estimand
is_significant
model
model_config
model_options
n_control
n_treated
outcome
p_value
summary
Return a summary DataFrame of the results.
Returns
DataFrame– Summary DataFrame.