Estimates nonlinear causal dose-response functions for continuous
    treatments using spline-based methods under standard causal assumptions
    (unconfoundedness / ignorability). Implements three identification
    strategies: Inverse Probability Weighting (IPW) via the generalised
    propensity score (GPS), G-computation (outcome regression), and a
    doubly-robust combination. Natural cubic splines and B-splines are
    supported for both the exposure-response curve f(T) and the propensity
    nuisance model. Pointwise confidence bands are obtained via the sandwich
    estimator or nonparametric bootstrap. Also provides fragility diagnostics
    including pointwise curvature-based fragility, uncertainty-normalised
    fragility, and regional integration over user-defined treatment intervals.
    Builds on the framework of Hirano and Imbens (2004)
    <doi:10.1111/j.1468-0262.2004.00481.x> for continuous treatments and
    extends it to fully nonparametric spline estimation.

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects based on different identification strategies (unconfoundedness, instruments, difference-in-differences, regression discontinuity designs).

Based on “Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis” (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit.

This tool performs pairwise correlation analysis and estimate causality.
    Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.

While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).