Beyond Actual Difference Making

One view of what links a cause to an effect is that causes make a difference to whether or not the effect is produced. This assumption is behind comparative studies, such as the method of randomized controlled trials, aimed at showing whether a trial intervention makes a positive difference to outcomes. Comparative studies are regarded as the gold standard in some areas of research but they are also problematic. There can be causes that make no difference and some difference-makers that are not causes. This indicates that difference-making should be taken as a symptom of causation: a feature that accompanies it in some, though not all, cases. Symptoms can be useful in the discovery of causes but they cannot be definitive of causation.

Download Full-text

Act Consequentialism and the No-Difference Challenge

The Oxford Handbook of Consequentialism ◽

10.1093/oxfordhb/9780190905323.013.29 ◽

2020 ◽

pp. 633-654

Author(s):

Holly Lawford-Smith ◽

William Tuckwell

Keyword(s):

Climate Change ◽

Real World ◽

Global Poverty ◽

Difference Making ◽

Act Consequentialism

According to act-consequentialism, only actions that make a difference to an outcome can be morally bad. Yet, there are classes of actions that don’t make a difference, but nevertheless seem to be morally bad. Explaining how such non-difference making actions are morally bad presents a challenge for act-consequentialism: the no-difference challenge. In this chapter we go into detail on what the no-difference challenge is, focusing in particular on act consequentialism. We talk about how different theories of causation affect the no-difference challenge; how the challenge shows up in real-world cases, including voting, global labor injustice, global poverty, and climate change; and we work through a number of the solutions to the challenge that have been offered, arguing that many fail to actually meet it. We defend and extend one solution that does, and we present a further solution of our own.

Download Full-text

Epistemic values of quantity and variety of evidence in biological mechanism research

European Journal for Philosophy of Science ◽

10.1007/s13194-021-00369-2 ◽

2021 ◽

Vol 11 (2) ◽

Author(s):

Yin Chung Au

Keyword(s):

Experimental Data ◽

Causal Inference ◽

Case Studies ◽

Biological Mechanism ◽

Data Generation ◽

Extended Version ◽

Epistemic Values ◽

Difference Making ◽

Mechanism Research ◽

The Difference

AbstractThis paper proposes an extended version of the interventionist account for causal inference in the practical context of biological mechanism research. This paper studies the details of biological mechanism researchers’ practices of assessing the evidential legitimacy of experimental data, arguing why quantity and variety are two important criteria for this assessment. Because of the nature of biological mechanism research, the epistemic values of these two criteria result from the independence both between the causation of data generation and the causation in question and between different interventions, not techniques. The former independence ensures that the interventions in the causation in question are not affected by the causation that is responsible for data generation. The latter independence ensures the reliability of the final mechanisms not only in the empirical but also the formal aspects. This paper first explores how the researchers use quantity to check the effectiveness of interventions, where they at the same time determine the validity of the difference-making revealed by the results of interventions. Then, this paper draws a distinction between experimental interventions and experimental techniques, so that the reliability of mechanisms, as supported by the variety of evidence, can be safely ensured in the probabilistic sense. The latter process is where the researchers establish evidence of the mechanisms connecting the events of interest. By using case studies, this paper proposes to use ‘intervention’ as the fruitful connecting point of literature between evidence and mechanisms.

Download Full-text

36 Effect of Blending and Tuning Relationship Matrices in Single-step Genomic BLUP

Journal of Animal Science ◽

10.1093/jas/skab235.032 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 19-20

Author(s):

Taylor M McWhorter ◽

Andre Garcia ◽

Matias Bermann ◽

Andres Legarra ◽

Ignacio Aguilar ◽

...

Keyword(s):

Single Step ◽

Fixation Index ◽

Genomic Information ◽

Software Suite ◽

Genetic Base ◽

Actual Difference ◽

The Impact ◽

Genomic Predictions

Abstract Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was > 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.

Download Full-text

If Counterfactuals Were Excluded from Historical Reasoning . . .

Journal of the Philosophy of History ◽

10.1163/18722636-12341342 ◽

2016 ◽

Vol 10 (3) ◽

pp. 370-381 ◽

Cited By ~ 4

Author(s):

Yemima Ben-Menahem

Keyword(s):

Relative Weight ◽

Historical Reasoning ◽

Difference Making ◽

Making A Difference ◽

Historical Necessity

The argument of this paper is that counterfactuals are indispensable in reasoning in general and historical reasoning in particular. It illustrates the role of counterfactuals in the study of history and explores the connection between counterfactuals and the notions of historical necessity and contingency. Entertaining alternatives to the actual course of events is conducive to the assessment of the relative weight and impact of the various factors that combine to bring about a certain result. Counterfactuals are essentially involved in understanding what it means for an event, an action, or an individual to make a difference. Making a difference, in turn, is shown to be a central category of historical reasoning. Counterfactuals, though sensitive to the description they use, make objective claims that can be confirmed or disconfirmed by evidence.

Download Full-text