Determining the best management practices for plant invasions is a critical, but often elusive goal. Invasive removals frequently involve complex and poorly understood biotic interactions. For example, invasive species can leave potent legacies that influence the success of native species restoration efforts, and positive plant‐microbial feedbacks may promote continued reinvasion by an exotic species following restoration. Removal methods can vary in their effects on plant–soil feedbacks, with consequences for restoration of native species. We determined the effects of invasion by a leguminous shrub (French broom;
- PAR ID:
- 10113291
- Date Published:
- Journal Name:
- Proceedings of the ACM on human-computer interaction
- ISSN:
- 2573-0142
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual‐measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems.
Methods This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment. Confirmed child maltreatment and internalizing and externalizing behaviors were recorded every 2 years between ages 4 and 16. Contamination in the nonmaltreated comparison group was identified and controlled by either a prospective self‐report assessment at ages 12, 14, and 16 or by a one‐time retrospective self‐report assessment at age 18. Synthetic control methods were used to establish causal effects and quantify the impact of contamination when it was not controlled, when it was controlled for by prospective self‐reports, and when it was controlled for by retrospective self‐reports.
Results Rates of contamination ranged from 62% to 67%. Without controlling for contamination, causal effect size estimates for internalizing behaviors were not statistically significant. Causal effects only became statistically significant after controlling contamination identified from either prospective or retrospective reports and effect sizes increased by between 17% and 54%. Controlling contamination had a smaller impact on effect size increases for externalizing behaviors but did produce a statistically significant overall effect, relative to the model ignoring contamination, when prospective methods were used.
Conclusions The presence of contamination in a nonmaltreated comparison group can underestimate the magnitude and statistical significance of causal effect size estimates, especially when investigating internalizing behavior problems. Addressing contamination can facilitate the replication of results across studies.
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Abstract Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well‐known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals outperform traditional instrumental variable confidence intervals when invalid instruments are present and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low‐density lipoprotein on globulin levels.
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Edward N. Zalta & Uri Nodelman (Ed.)This entry discusses some accounts of causal explanation developed after approximately 1990. Our focus in this entry is on the following three accounts – Section 1 those that focus on mechanisms and mechanistic explanations, Section 2 the kairetic account of explanation, and Section 3 interventionist accounts of causal explanation. All of these have as their target explanations of why or perhaps how some phenomenon occurs (in contrast to, say, explanations of what something is, which is generally taken to be non-causal) and they attempt to capture causal explanations that aim at such explananda. Section 4 then takes up some recent proposals having to do with how causal explanations may differ in explanatory depth or goodness. Section 5 discusses some issues having to do with what is distinctive about causal (as opposed to non-causal) explanations.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3359265
