Pseudo population dataset is computed based on user-defined causal inference approaches (e.g., matching or weighting). A covariate balance test is performed on the pseudo population dataset. Users can specify covariate balance criteria and activate an adaptive approach and number of attempts to search for a target pseudo population dataset that meets the covariate balance criteria.

Input parameters:

** Y** A data.frame of observed outcome,
including

`id`

and `Y`

columns.`w`

`id`

and `w`

columns.
`c`

`id`

column,`ci_appr`

`dist_measure`

`scale`

`delta_n`

`covar_bl_method`

`covar_bl_trs`

`max_attempt`

The matching algorithm aims to match an observed unit \(j\) to each \(j'\) at each exposure level \(w^{(l)}\).

We specify

(\(\delta_n\)), a caliper for any exposure level \(w\), which constitutes equally sized bins, i.e., \([w-\delta_n, w+\delta_n]\). Based on the caliper`delta_n`

, we define a predetermined set of \(L\) exposure levels \(\{w^{(1)}=\min(w)+ \delta_n,w^{(2)}=\min(w)+3 \delta_n,...,w^{(L)} = \min(w)+(2L-1) \delta_n\}\), where \(L = \lfloor \frac{\max(w)-\min(w)}{2\delta_n} + \frac{1}{2} \rfloor\). Each exposure level \(w^{(l)}\) is the midpoint of equally sized bins, \([w^{(l)}-\delta_n, w^{(l)}+\delta_n]\).`delta_n`

We implement a nested-loop algorithm, with \(l\) in \(1,2,\ldots, L\) as the outer-loop, and \(j'\) in \(1 ,\ldots,N\) as the inner-loop. The algorithm outputs the final product of our design stage, i.e., a matched set with \(N\times L\) units.

**for**\(l = 1,2,\ldots, L\)**do**

Choose**one**exposure level of interest \(w^{(l)} \in \{w^{(1)}, w^{(2)}, ..., w^{(L)}\}\).

**for**\(j' = 1 ,\ldots,N\)**do**

2.1 Evaluate the GPS \(\hat{e}(w^{(l)}, \mathbf{c}_{j'})\) (for short \(e^{(l)}_{j'}\)) at \(w^{(l)}\) based on the fitted GPS model in Step 1 for each unit \(j'\) having observed covariates \(\mathbf{c}_{j'}\).

2.2 Implement the matching to find**an**observed unit – denoted by \(j\) – that matched with \(j'\) with respect to both the exposure \(w_{j}\approx w^{(l)}\) and the estimated GPS \(\hat{e}(w_j, \mathbf{c}_{j}) \approx e^{(l)}_{j'}\) (under a standardized Euclidean transformation). More specifically, we find a \(j\) as \[ j_{{gps}}(e^{(l)}_{j'},w^{(l)})=\text{arg} \ \underset{j: w_j \in [w^{(l)}-\delta_n,w^{(l)}+\delta_n]}{\text{min}} \ \mid\mid( \lambda \hat{e}^{*}(w_j,\mathbf{c}_j), (1-\lambda)w^{*}_j) -(\lambda e_{j'}^{(l)*}, (1-\lambda) w^{(l)*})\mid\mid, \] where(\(||.||\)) is a pre-specified two-dimensional metric,`dist_measure`

(\(\lambda\)) is the scale parameter assigning weights to the corresponding two dimensions (i.e., the GPS and exposure), and \(\delta\) is the caliper defined in Step 2 allowing that only the unit \(j\) with an observed exposure \(w_j \in [w^{(l)}-\delta,w^{(l)}+\delta]\) can get matched.`scale`

2.3 Impute \(Y_{j'}(w^{(l)})\) as: \(\hat{Y}_{j'}(w^{(l)})=Y^{obs}_{j_{{gps}}(e^{(l)}_{j'},w^{(l)})}\).

**end for****end for**After implementing the matching algorithm, we construct the matched set with \(N\times L\) units by combining all \(\hat{Y}_{j'}(w^{(l)})\) for \(j'=1,\ldots,N\) and for all \(w^{(l)} \in \{w^{(1)},w^{(2)},...,w^{(L)}\}\).

We introduce the absolute correlation measure
(** covar_bl_method** = “absolute”) to assess
covariate balance for continuous exposures . The absolute correlation
between the exposure and each pre-exposure covariate is a global measure
and can inform whether the whole matched set is balanced. The measures
above build upon the work by (Austin 2019)
who examine covariate balance conditions with continuous exposures. We
adapt them into the proposed matching framework.

In a balanced pseudo population dataset, the correlations between the
exposure and pre-exposure covariates should close to zero, that is \(E [\mathbf{c}_{i}^{*} w_{i}^{*} ] \approx
\mathbf{0}.\) We calculate the absolute correlation in the pseudo
population dataset as

\[\begin{align*}
\big\lvert \sum_{i=1}^{N\times L} \mathbf{c}_{i}^{*} w_{i}^{*}
\big\lvert
\end{align*}\]

The average absolute correlations are defined as the average of
absolute correlations among all covariates. Average absolute
correlation: \[\begin{align*}
\overline{\big\lvert \sum_{i=1}^{N\times L}
\mathbf{c}_{i}^{*} w_{i}^{*} \big\lvert} < \boldsymbol{\epsilon}_1.
\end{align*}\] We specify a pre-specified threshold
** covar_bl_trs** (\(\boldsymbol{\epsilon}_1\)), for example
0.1, on average absolute correlation as the threshold for covariate
balance in the pseudo population dataset.

Austin, Peter C. 2019. “Assessing Covariate Balance When Using the
Generalized Propensity Score with Quantitative or Continuous
Exposures.” *Statistical Methods in Medical Research* 28
(5): 1365–77.