Causal inference for time-varying treatments in latent Markov models: An application to the effects of remittances on poverty dynamics

Tullio, Federico; Bartolucci, Francesco

doi:10.1214/21-aoas1578

Cited by 5 publications

(3 citation statements)

References 61 publications

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“…Following work by Bartolucci et al (2016) and Tullio and Bartolucci (2022), we reformulate the LMM in terms of the potential outcomes framework for defining meaningful estimands in LMMs. Under conditional exchangeability, we define the ATE at 𝑡 as…”

Section: Causal Estimands For Latent Markov Modelsmentioning

confidence: 99%

“…Lanza et al (2013) and Clouth et al (2022) and proposed different approaches for using IPW to estimate the causal effect of an exposure on class membership in a cross-sectional LCA. The approach by Lanza and colleagues was extended for longitudinal settings with multiple (time-constant) exposures (Bartolucci et al, 2016) and time-varying exposures (Tullio & Bartolucci, 2022). Recently, Bartolucci et al (2023) proposed a new causal latent transition model inspired by the difference-in-differences approach (Imbens & Wooldridge, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Causal Inference for Latent Markov Models Using the Parametric G-Formula

Clouth,

Bijlsma,

Pauws

et al. 2023

Preprint

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The parametric g-formula can be used to estimate causal effects of time-varying exposures or treatments on observable outcomes. In such a longitudinal setting, time-varying confounders that are affected by prior exposures need to be adjusted for. The parametric g-formula does this by specifying several parametric models, one each for every time-varying variable, and by performing micro-simulations. However, its restriction to use cases of observable outcomes limits the possibility for applications of the parametric g-formula in the social sciences. In the social sciences, many variables of interest are unobservable constructs. In such cases, measurement models are needed. Here, we propose a new approach of using bias-adjusted three-step latent Markov models (LMMs) within the parametric g-formula. LMMs estimate the probability of membership in an unobservable state based on a number of observed indicator variables. Additionally, estimates for the probabilities of transitions between these latent states are obtained. We show that by replacing the parametric models in the g-formula with LMMs, the micros-simulations can be performed as usual. We illustrate the use of this new approach by estimating the average treatment effect of unemployment on the probabilities for several mental health states utilizing longitudinal data from the LISS panel.

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Section: Causal Estimands For Latent Markov Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Causal Inference for Latent Markov Models Using the Parametric G-Formula

Clouth,

Bijlsma,

Pauws

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…More specifically, Lanza et al (2013) proposed a one-step method where a latent class model with treatment as the only covariate is estimated on a dataset that has been weighted with the inverse of the propensity score. This approach has been extended for longitudinal settings (Bartolucci et al, 2016;Tullio & Bartolucci, 2019 and for LCA with distal outcomes (Bray et al, 2019;Schuler et al, 2014;Yamaguchi, 2015). Recently, Clouth et al (2022) proposed a three-step approach where the measurement model is estimated on the unweighted data and IPW is introduced in the third step in which the treatment effect is estimated.…”

Section: Introductionmentioning

confidence: 99%

Three-step latent class analysis with inverse propensity weighting in the presence of differential item functioning

Clouth¹,

Pauws²,

Vermunt³

2022

Preprint

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Bias-adjusted three-step latent class analysis (LCA) is a popular tool to relate external variables to latent class membership. The integration of causal inference techniques such as inverse propensity weighting (IPW) with LCA allows for addressing causal questions about the relationship between these external variables and the latent classes even when data is collected in an observational design. However, LCA’s key assumption of conditional independence between external variables and latent class indicators is often violated in practice. This is the case when external variables have direct effects on (some of) the latent class indicators, i.e., when a (nominal) covariate represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). Following the approach by Vermunt and Magidson (2021), we propose a modification of the bias-adjusted three-step LCA with IPW to account for DIF. Covariates causing DIF, specifically the treatment or exposure variable, should be included in the first step, that is, the estimation of the measurement model. Furthermore, in the third step, that is, the estimation of the structural model which includes IPW, the step-three classification error adjustment needs to be allowed to differ across the values of the DIF covariates. Additionally, we propose a modelbuilding strategy to identify if the treatment or exposure variable shows MNI or DIF. Our newly proposed approach is illustrated using a synthetic and a real-life data example and is implemented in the program Latent GOLD.

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Intergenerational mobility measurement with latent transition matrices

Bavaro

Tullio

2022

J Econ Inequal

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We propose a multivariate approach for the estimation of intergenerational transition matrices. Our methodology is grounded on the assumption that individuals’ social status is unobservable and must be estimated. In this framework, parents and offspring are clustered on the basis of the observed levels of income and occupational categories, thus avoiding any discretionary rule in the definition of class boundaries. The resulting transition matrix is a function of the posterior probabilities of parents and young adults of belonging to each class. Estimation is carried out via maximum likelihood by means of an expectation-maximization algorithm. We illustrate the proposed method using National Longitudinal Survey Data from the United States in the period 1978-2006.

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Causal inference for time-varying treatments in latent Markov models: An application to the effects of remittances on poverty dynamics

Cited by 5 publications

References 61 publications

Causal Inference for Latent Markov Models Using the Parametric G-Formula

Causal Inference for Latent Markov Models Using the Parametric G-Formula

Three-step latent class analysis with inverse propensity weighting in the presence of differential item functioning

Intergenerational mobility measurement with latent transition matrices

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