| 1. |
- de Luna, Xavier, 1968-, et al.
(författare)
-
Testing for the Unconfoundedness Assumption Using an Instrumental Assumption
- 2014
-
Ingår i: Journal of Causal Inference. - : Walter de Gruyter GmbH. - 2193-3677 .- 2193-3685. ; 2:2, s. 187-199
-
Tidskriftsartikel (refereegranskat)abstract
- The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.
|
|
| 2. |
- Gabriel, Erin E., et al.
(författare)
-
Bias attenuation results for dichotomization of a continuous confounder
- 2022
-
Ingår i: Journal of Causal Inference. - : DE GRUYTER POLAND SP Z O O. - 2193-3677 .- 2193-3685. ; 10:1, s. 515-526
-
Tidskriftsartikel (refereegranskat)abstract
- It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation. There are additional examples in the literature where adjusting for a dichotomized confounder can be more biased than not adjusting at all. The message is clear, do not dichotomize. What is unclear is if there are scenarios where adjusting for the dichotomized confounder always leads to lower bias than not adjusting. We propose several sets of conditions that characterize scenarios where one should always adjust for the dichotomized confounder to reduce bias. We then highlight scenarios where the decision to adjust should be made more cautiously. To our knowledge, this is the first formal presentation of conditions that give information about when one should and potentially should not adjust for a dichotomized confounder.
|
|
| 3. |
|
|
| 4. |
- Nordin, Mattias, 1984-, et al.
(författare)
-
Properties of restricted randomization with implications for experimental design
- 2022
-
Ingår i: Journal of Causal Inference. - : Walter de Gruyter. - 2193-3677 .- 2193-3685. ; 10:1, s. 227-245
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, there has been increasing interest in the use of heavily restricted randomization designs which enforce balance on observed covariates in randomized controlled trials. However, when restrictions are strict, there is a risk that the treatment effect estimator will have a very high mean squared error (MSE). In this article, we formalize this risk and propose a novel combinatoric-based approach to describe and address this issue. First, we validate our new approach by re-proving some known properties of complete randomization and restricted randomization. Second, we propose a novel diagnostic measure for restricted designs that only use the information embedded in the combinatorics of the design. Third, we show that the variance of the MSE of the difference-in-means estimator in a randomized experiment is a linear function of this diagnostic measure. Finally, we identify situations in which restricted designs can lead to an increased risk of getting a high MSE and discuss how our diagnostic measure can be used to detect such designs. Our results have implications for any restricted randomization design and can be used to evaluate the trade-off between enforcing balance on observed covariates and avoiding too restrictive designs.
|
|
| 5. |
- Pena, Jose M
(författare)
-
Bounding the probabilities of benefit and harm through sensitivity parameters and proxies
- 2023
-
Ingår i: Journal of Causal Inference. - : DE GRUYTER POLAND SP Z O O. - 2193-3677 .- 2193-3685. ; 11:1
-
Tidskriftsartikel (refereegranskat)abstract
- We present two methods for bounding the probabilities of benefit (a.k.a. the probability of necessity and sufficiency, i.e., the desired effect occurs if and only if exposed) and harm (i.e., the undesired effect occurs if and only if exposed) under unmeasured confounding. The first method computes the upper or lower bound of either probability as a function of the observed data distribution and two intuitive sensitivity parameters, which can then be presented to the analyst as a 2-D plot to assist in decision-making. The second method assumes the existence of a measured nondifferential proxy for the unmeasured confounder. Using this proxy, tighter bounds than the existing ones can be derived from just the observed data distribution.
|
|
| 6. |
- Pena, Jose M, et al.
(författare)
-
On the bias of adjusting for a non-differentially mismeasured discrete confounder
- 2021
-
Ingår i: Journal of Causal Inference. - : Walter de Gruyter. - 2193-3677 .- 2193-3685. ; 9:1, s. 229-249
-
Tidskriftsartikel (refereegranskat)abstract
- Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that adjustment for the mismeasured observed variables provides a less biased estimate of the true average causal effect than not adjusting. However, this is not always the case and depends on both the nature of the measurement and confounding. We describe two sets of conditions under which adjusting for a non-deferentially mismeasured proxy comes closer to the unidentifiable true average causal effect than the unadjusted or crude estimate. The first set of conditions apply when the exposure is discrete or continuous and the confounder is ordinal, and the expectation of the outcome is monotonic in the confounder for both treatment levels contrasted. The second set of conditions apply when the exposure and the confounder are categorical (nominal). In all settings, the mismeasurement must be non-differential, as differential mismeasurement, particularly an unknown pattern, can cause unpredictable results.
|
|
| 7. |
- Pena, Jose M
(författare)
-
On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder
- 2020
-
Ingår i: Journal of Causal Inference. - : Walter de Gruyter. - 2193-3677 .- 2193-3685. ; 8:1, s. 150-163
-
Tidskriftsartikel (refereegranskat)abstract
- Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed. We show that, under certain monotonicity assumption that is empirically verifiable, adjusting for the proxy produces a measure of the effect that is between the unadjusted and the true measures.
|
|
| 8. |
- Pena, Jose M
(författare)
-
Simple yet sharp sensitivity analysis for unmeasured confounding
- 2022
-
Ingår i: Journal of Causal Inference. - : Walter de Gruyter GmbH. - 2193-3677 .- 2193-3685. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure-outcome confounding.
|
|
| 9. |
- Pena, Jose M
(författare)
-
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
- 2020
-
Ingår i: Journal of Causal Inference. - : WALTER DE GRUYTER GMBH. - 2193-3677 .- 2193-3685. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.
|
|
| 10. |
- Sjolander, A
(författare)
-
A note on a sensitivity analysis for unmeasured confounding, and the related E-value
- 2020
-
Ingår i: JOURNAL OF CAUSAL INFERENCE. - : Walter de Gruyter GmbH. - 2193-3677 .- 2193-3685. ; 8:1, s. 229-248
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal strength of association that an unmeasured confounder may have with the exposure and with the outcome, respectively. The E-value is defined as the strength of association that the confounder must have with the exposure and the outcome, to fully explain away an observed exposure-outcome association. We derive the feasible region of the sensitivity analysis parameters, and we show that the bounds produced by the sensitivity analysis are not always sharp. We finally establish a region in which the bounds are guaranteed to be sharp, and we discuss the implications of this sharp region for the interpretation of the E-value. We illustrate the theory with a real data example and a simulation.
|
|
