Author: Wenbo Lyu

# Synergistic-Unique-Redundant Decomposition of Causality (SURD)

#### Wenbo Lyu

####
Last update: 2026-03-30
Last run: 2026-03-30

# Introduction

Understanding how causal influences combine and interact is essential for both temporal and spatial systems. The Synergistic–Unique–Redundant Decomposition (SURD) framework provides a principled way to break down total information flow between variables into:

- Unique contributions — information uniquely provided by one driver;
- Redundant contributions — information shared by multiple drivers;
- Synergistic contributions — information only revealed when drivers are considered together.

This vignette demonstrates how to perform SURD analysis using the `infoxtr` package. We will explore both temporal and spatial cases, showing how `infoxtr` unifies time-series and spatial cross-sectional causal analysis via consistent interfaces to `data.frame`, `sf`, and `SpatRaster` objects.

# Utility Functions

To simplify visualization, a plotting function is provided below, which produces a SURD decomposition plot — a grouped bar chart of unique (U), synergistic (S), and redundant (R) components, along with a side panel showing the information leak.

    utils_plot_surd = \(surd_list,threshold = 0,style = "shallow") {
      df = surd_list |>
        tibble::as_tibble() |>
        dplyr::filter(vars != "InfoLeak")

      if(threshold > 0){
        df = dplyr::filter(df, values > threshold)
      }

      df = df |>
        dplyr::mutate(
          var_nums = stringr::str_extract_all(vars, "(?<=V)\\d+"),
          nums_joined = sapply(var_nums, \(x) paste(x, collapse = '*","*')),
          vars_formatted = paste0(types, "[", nums_joined, "]"),
          type_order = dplyr::case_when(
            types == "U" ~ 1,
            types == "S" ~ 2,
            types == "R" ~ 3
          ),
          var_count = lengths(var_nums)
        )  |>
        dplyr::arrange(type_order, var_count) |>
        dplyr::mutate(vars = factor(vars_formatted, levels = unique(vars_formatted)))

      if (style == "shallow") {
        colors = c(U = "#ec9e9e", S = "#fac58c", R = "#668392", InfoLeak = "#7f7f7f")
      } else {
        colors = c(U = "#d62828", S = "#f77f00", R = "#003049", InfoLeak = "gray")
      }

      p1 = ggplot2::ggplot(df, ggplot2::aes(x = vars, y = values, fill = types)) +
        ggplot2::geom_col(color = "black", linewidth = 0.15, show.legend = FALSE) +
        ggplot2::scale_fill_manual(name = NULL, values = colors) +
        ggplot2::scale_x_discrete(name = NULL, labels = function(x) parse(text = x)) +
        ggplot2::scale_y_continuous(name = NULL, limits = c(0, 1), expand = c(0, 0),
                                    breaks = c(0, 0.25, 0.5, 0.75, 1),
                                    labels = c("0", "0.25", "0.5", "0.75", "1") ) +
        ggplot2::theme_bw() +
        ggplot2::theme(
          axis.ticks.x = ggplot2::element_blank(),
          axis.text.x = ggplot2::element_text(angle = 45, hjust = 1),
          panel.grid = ggplot2::element_blank(),
          panel.border = ggplot2::element_rect(color = "black", linewidth = 2)
        )

      df_leak = dplyr::slice_tail(tibble::as_tibble(surd_list), n = 1)
      p2 = ggplot2::ggplot(df_leak, ggplot2::aes(x = vars, y = values)) +
        ggplot2::geom_col(fill = colors[4], color = "black", linewidth = 0.15) +
        ggplot2::scale_x_discrete(name = NULL, expand = c(0, 0)) +
        ggplot2::scale_y_continuous(name = NULL, limits = c(0, 1), expand = c(0, 0),
                                    breaks = c(0,1), labels = c(0,1) ) +
        ggplot2::theme_bw() +
        ggplot2::theme(
          axis.ticks.x = ggplot2::element_blank(),
          panel.grid = ggplot2::element_blank(),
          panel.border = ggplot2::element_rect(color = "black", linewidth = 2)
        )

      patchwork::wrap_plots(p1, p2, ncol = 2, widths = c(10,1))
    }

To generate the visualization, please ensure the required packages are installed by running the following code:

    if (!requireNamespace("dplyr")) install.packages("dplyr")
    if (!requireNamespace("tibble")) install.packages("tibble")
    if (!requireNamespace("stringr")) install.packages("stringr")
    if (!requireNamespace("ggplot2")) install.packages("ggplot2")
    if (!requireNamespace("patchwork")) install.packages("patchwork")

# Example Cases

The following sections demonstrate SURD decomposition in different contexts, illustrating its flexibility across temporal and spatial causal analyses.

## Air Pollution and Cardiovascular Health in Hong Kong

    cvd = readr::read_csv(system.file("case/cvd.csv",package = "tEDM"))
    head(cvd)
    ## # A tibble: 6 × 5
    ##     cvd   rsp   no2   so2    o3
    ##   <dbl> <dbl> <dbl> <dbl> <dbl>
    ## 1   214  73.7  74.5  19.1 17.4
    ## 2   203  77.6  80.9  18.8 39.4
    ## 3   202  64.8  67.1  13.8 56.4
    ## 4   182  68.8  74.7  30.8  5.6
    ## 5   181  49.4  62.3  23.1  3.6
    ## 6   129  67.4  63.6  17.4  6.73

    cvd_long = cvd |>
      tibble::rowid_to_column("id") |>
      tidyr::pivot_longer(cols = -id,
                          names_to = "variable", values_to = "value")

    fig_cvds_ts = ggplot2::ggplot(cvd_long, ggplot2::aes(x = id, y = value, color = variable)) +
      ggplot2::geom_line(linewidth = 0.5) +
      ggplot2::labs(x = "Days (from 1995-3 to 1997-11)", y = "Concentrations or \nNO. of CVD admissions", color = "") +
      ggplot2::theme_bw() +
      ggplot2::theme(legend.direction = "horizontal",
                     legend.position = "inside",
                     legend.justification = c("center", "top"),
                     legend.background = ggplot2::element_rect(fill = "transparent", color = NA))
    fig_cvds_ts

Figure 1. Time series of air pollutants and confirmed CVD cases in Hong Kong from March 1995 to November 1997.

To investigate the causal influences of air pollutants on the incidence of cardiovascular diseases, we performed SURD analysis using a time lag step `3` and `10` discretization bins.

    res_cvds = infoxtr::surd(cvd, 1, 2:5,
                             lag = 15, bin = 10, threads = 6)
    tibble::as_tibble(res_cvds)
    ## # A tibble: 20 × 3
    ##    vars        types      values
    ##    <chr>       <chr>       <dbl>
    ##  1 V1          U        0.00219
    ##  2 V2          U        0.0155
    ##  3 V3          U        0.00252
    ##  4 V1_V2       R        0.00345
    ##  5 V1_V4       R        0.000711
    ##  6 V1_V2_V3    R        0.00506
    ##  7 V1_V2_V4    R        0.00991
    ##  8 V1_V2_V3_V4 R        0.0269
    ##  9 V1_V2       S        0.0205
    ## 10 V1_V3       S        0.0224
    ## 11 V1_V4       S        0.0451
    ## 12 V2_V3       S        0.0256
    ## 13 V2_V4       S        0.0352
    ## 14 V3_V4       S        0.0366
    ## 15 V1_V2_V3    S        0.0350
    ## 16 V1_V2_V4    S        0.104
    ## 17 V1_V3_V4    S        0.151
    ## 18 V2_V3_V4    S        0.0467
    ## 19 V1_V2_V3_V4 S        0.404
    ## 20 InfoLeak    InfoLeak 0.535

The SURD results are shown in the figure below:

    utils_plot_surd(res_cvds)

Figure 2. SURD decomposition results between different air pollutants and cardiovascular diseases.

## Population Density and Its Drivers in Mainland China

    popd_nb = spdep::read.gal(system.file("case/popd_nb.gal",package = "spEDM"))
    ## Warning in spdep::read.gal(system.file("case/popd_nb.gal", package = "spEDM")): neighbour
    ## object has 4 sub-graphs
    popd = readr::read_csv(system.file("case/popd.csv",package = "spEDM"))
    popd_sf = sf::st_as_sf(popd, coords = c("lon","lat"), crs = 4326)
    popd_sf
    ## Simple feature collection with 2806 features and 5 fields
    ## Geometry type: POINT
    ## Dimension:     XY
    ## Bounding box:  xmin: 74.9055 ymin: 18.2698 xmax: 134.269 ymax: 52.9346
    ## Geodetic CRS:  WGS 84
    ## # A tibble: 2,806 × 6
    ##     popd  elev   tem   pre slope          geometry
    ##  * <dbl> <dbl> <dbl> <dbl> <dbl>       <POINT [°]>
    ##  1  780.     8  17.4 1528. 0.452 (116.912 30.4879)
    ##  2  395.    48  17.2 1487. 0.842 (116.755 30.5877)
    ##  3  261.    49  16.0 1456. 3.56  (116.541 30.7548)
    ##  4  258.    23  17.4 1555. 0.932  (116.241 30.104)
    ##  5  211.   101  16.3 1494. 3.34   (116.173 30.495)
    ##  6  386.    10  16.6 1382. 1.65  (116.935 30.9839)
    ##  7  350.    23  17.5 1569. 0.346 (116.677 30.2412)
    ##  8  470.    22  17.1 1493. 1.88  (117.066 30.6514)
    ##  9 1226.    11  17.4 1526. 0.208 (117.171 30.5558)
    ## 10  137.   598  13.9 1458. 5.92  (116.208 30.8983)
    ## # ℹ 2,796 more rows

    res_popd = infoxtr::surd(popd_sf, 1, 2:5,
                             lag = 3, bin = 10, nb = popd_nb, threads = 6)
    tibble::as_tibble(res_popd)
    ## # A tibble: 19 × 3
    ##    vars        types     values
    ##    <chr>       <chr>      <dbl>
    ##  1 V1          U        0.00453
    ##  2 V3          U        0.0390
    ##  3 V4          U        0.00349
    ##  4 V1_V3       R        0.00872
    ##  5 V3_V4       R        0.00269
    ##  6 V1_V2_V3    R        0.00546
    ##  7 V1_V2_V3_V4 R        0.110
    ##  8 V1_V2       S        0.0361
    ##  9 V1_V3       S        0.0461
    ## 10 V1_V4       S        0.0866
    ## 11 V2_V3       S        0.0458
    ## 12 V2_V4       S        0.0749
    ## 13 V3_V4       S        0.0858
    ## 14 V1_V2_V3    S        0.0376
    ## 15 V1_V2_V4    S        0.0584
    ## 16 V1_V3_V4    S        0.0453
    ## 17 V2_V3_V4    S        0.154
    ## 18 V1_V2_V3_V4 S        0.107
    ## 19 InfoLeak    InfoLeak 0.642

    utils_plot_surd(res_popd)

Figure 3. SURD decomposition results between population density and its Drivers.

## Influence of Climatic and Topographic Factors on Net Primary Productivity (NPP) in Mainland China

    npp = terra::rast(system.file("case/npp.tif", package = "spEDM"))
    npp
    ## class       : SpatRaster
    ## size        : 404, 483, 5  (nrow, ncol, nlyr)
    ## resolution  : 10000, 10000  (x, y)
    ## extent      : -2625763, 2204237, 1877078, 5917078  (xmin, xmax, ymin, ymax)
    ## coord. ref. : CGCS2000_Albers
    ## source      : npp.tif
    ## names       :      npp,        pre,      tem,      elev,         hfp
    ## min values  :   164.00,   384.3409, -47.8194, -122.2004,  0.03390418
    ## max values  : 16606.33, 23878.3555, 263.6938, 5350.4902, 44.90312195

    res_npp = infoxtr::surd(npp,1, 2:5,
                            lag = 2, bin = 10, threads = 6)
    tibble::as_tibble(res_npp)
    ## # A tibble: 22 × 3
    ##    vars        types    values
    ##    <chr>       <chr>     <dbl>
    ##  1 V1          U     0.0777
    ##  2 V2          U     0.000234
    ##  3 V3          U     0.0000114
    ##  4 V4          U     0.0164
    ##  5 V1_V2       R     0.0948
    ##  6 V1_V3       R     0.000828
    ##  7 V1_V4       R     0.00514
    ##  8 V1_V2_V3    R     0.0137
    ##  9 V1_V2_V4    R     0.0149
    ## 10 V1_V2_V3_V4 R     0.565
    ## # ℹ 12 more rows

    utils_plot_surd(res_npp)

Figure 4. SURD decomposition results between climatic and topographic factors and npp.

🧩 See also:

- `?infoxtr::surd()` for function details.
- `spEDM` package for spatial empirical dynamic modeling.
- `tEDM` package for temporal empirical dynamic modeling.