Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments

By Miguel A. Hern'an, Babette A. Brumback, James M. Robins. 2001

526 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

In Causal Inference & Discovery

Citations in the corpus

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “We adopt the implementation in (Hernán et al., 2001; Howe et al., 2012; Lim et al., 2018) for MSMs and use logistic regression for estimating the propensity weights as follows: where ω (cid:63) , φ (cid:63) and w (cid:63) are regression coefﬁcients, k ∈ { c, d indicates the chemotherapy or…”
Learning When-to-Treat Policies (2019, methodology): “To identify causal effects, we make the standard assumptions of sequential ignorability, consistency and overlap (Robins 1986, 2004; Hernán, Brumback, and Robins 2001; Murphy 2003).”
Estimating dynamic treatment effects in event studies with heterogeneous treatment effects (2018, methodology): “The literature on estimating treatment effects of a time-varying treatment starts with the marginal structural models developed by Hernán et al. (2001). They take a selection-on-observables approach, by assuming sequential randomization conditional on timevarying observable confounders. Event studie”
Industry collaboration, scientific sharing, and the dissemination of knowledge (2010, methodology): “In an attempt to correct for violations of this assumption, I produced consistent findings combining both fixed and random effect specification with Inverse Probability of Treatment Weighted (IPTW) estimation (Azoulay et al., 2006; Hernán et al., 2001; Robins et al., 2000).”
Industry Induces Academic Science to Know Less about More1 (2010, methodology): “…of this assumption, I produced consistent findings combining the fixed-effect specification with an approach that has recently gained acceptance in biostatistics: inverse probability of treatment weighted (IPTW) estimation (Robins, Hernán, and Brumback 2000; Hernán, Brumback, and Robins 2001).”
The Impact of Academic Patenting on the Rate, Quality and Direction of (Public) Research Output (2009, methodology): “In Figure 1, we plot the distribution of patents for the patenting researchers in our sample.”
The consistency statement in causal inference: a definition or an assumption? (2009, background): “rarely account for possible departures from the consistency assumption.(11,12,17) Some of these same applications routinely”
A Bayesian view of doubly robust causal inference (2016, methodology): “nal treatment assignment probabilities under O, which would result in the usual kind of stabilized inverse probability of treatment weights used in marginal structural modelling (Robins et al., 2000; Hernán et al., 2001; Cole and Hernán, 2008). The marginal causal contrast may now be estimated throu”
Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models (2013, background): “…time-dependent confounding is controlled by assigning a weight to each subject or observation which is inversely proportional to his probability of receiving the observed treatment given the past history of treatment and confounders as well as the baseline covariates (Hernán et al., 2000, 2001).”
Endogenous selection or treatment model estimation (2007, background): “Recent treatment related estimators include Imbens and Angrist (1994), Angrist and Imbens (1995), Heckman, Ichimura, and Todd (1998), Hahn (1998), Hirano, Imbens and Ridder (2000), Abadie (2001), and Hernan, Brumback, and Robins (2001).”
Estimation of the causal effects of time-varying exposures (2008, background): “…discussed in Robins (1997a, 1998); of (ii) in Robins, Rotnitzky, and Zhao (1995), Robins (2003), and van der Laan and Robins (2003); of (iii) in Robins (1994, 1997a) and Hernán, Brumback, and Robins (2001, 2002); and of (iv) in Robins, Rotnitzky, and Scharfstein (1999) and Brumback et al. (2004).”