# Frequently Asked Questions

# How does `multibias` fit into the causal inference workflow?

If the causal analysis process involves the following general steps:

1. Specify a causal question
2. Graph the causal assumptions (via a DAG)
3. Apply confounder balancing (matching, weighting, etc.)
4. Assess confounder balance
5. Estimate the causal effect
6. Perform sensitivity analysis of the effect estimate

Then `multibias` plugs into the final step of running sensitivity analyses. Traditionally, the sensitivity analyses involve re-estimating results under different assumptions related to the specified or unspecified confounders via technique such as E-values and tipping point analyses. However, we know that confounding is just one of several different types of biases that may impact causal estimates, and `multibias` helps fill this void. It does so by allowing for the simultaneous adjustment of a variety of biases: uncontrolled confounding, exposure misclassification, outcome misclassification, and selection bias. This allows for a more transparent and complete understanding of how all potential sources of systematic error may be impacting the analysis.

# My causal analysis leveraged propensity scores - how does that fit into a multibias analysis?

While propensity score methods are valuable for handling confounding by measured variables, they don’t address all potential sources of bias. This is where multibias analysis becomes a crucial next step.

A limitation of propensity scores is that they can only balance covariates that were actually measured and included in the propensity score model. A multibias analysis allows you to quantitatively assess the potential impact of such unmeasured confounding on top of the propensity score-adjusted analysis. In addition, propensity scores are designed specifically for confounding control. They do not inherently address other types of systematic error, such as selection bias and misclassification.

In practice, you would first conduct your primary analysis using propensity scores to control for measured confounders. You would then use `multibias` to perform a sensitivity analysis for unmeasured confounders and other potential sources of bias.

# What is bootstrapping and how is it relevant here?

Bootstrapping is a statistical technique used to estimate the uncertainty around an estimate. Instead of analyzing your study data once, bootstrapping involves repeatedly drawing random samples with replacement from your original data. Each of these samples is the same size as the original data. For each bootstrap sample, you then calculate the statistic of interest - a bias-adjusted estimate in the context of `multibias`. By repeating this resampling, you end up with a distribution of the statistic. From this distribution you can derive the confidence interval and standard error.

When `multibias` adjusts the data’s exposure-outcome effect for potential biases, the adjusted estimate has uncertainty. This uncertainty comes not only from the original random (sampling) error in the data, but also from the uncertainty in the bias parameters (systematic error). Bootstrapping is particularly important for `multibias` because it allows for confidence intervals that incorporate two sources of uncertainty: uncertainty due to random error and uncertainty due to systematic error.

This exercise of performing bias analysis with uncertainty in the bias parameters is called probabilistic bias analysis. When running `multibias_adjust()` with validation data (a `data_validation` object) across bootstrap samples, the function automatically resamples from the Normal distribution bias parameters (estimate and standard error) inferred from the validation data. When running `multibias_adjust()` with specified bias parameters (a `bias_params` object) across bootstrap samples, you must input the parameter values as statistical distributions (e.g., by using the `rnorm()` or `runif()` function).

# How do I cite `multibias`?

To get the most up-to-date citation information, please use the built-in `citation()` command in R.

    citation("multibias")

    ## To cite package 'multibias' in publications use:
    ##
    ##   Brendel P (2026). _multibias: Multiple Bias Analysis in Causal
    ##   Inference_. R package version 1.7.3,
    ##   http://www.paulbrendel.com/multibias/,
    ##   <https://github.com/pcbrendel/multibias>.
    ##
    ## A BibTeX entry for LaTeX users is
    ##
    ##   @Manual{,
    ##     title = {multibias: Multiple Bias Analysis in Causal Inference},
    ##     author = {Paul Brendel},
    ##     year = {2026},
    ##     note = {R package version 1.7.3, http://www.paulbrendel.com/multibias/},
    ##     url = {https://github.com/pcbrendel/multibias},
    ##   }

# Does `multibias` support time-to-event data and analyses?

Not currently